Alexey/IIS_2023_1
4
3
Fork 1
Code Issues Pull Requests 5 Packages Projects Releases Wiki Activity

Merge branch 'main' into malkova_anastasia_lab_1

Browse Source
This commit is contained in:
admal 2023-11-01 23:59:43 +04:00
commit 08ed6413b9
360 changed files with 662673 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
.idea/IIS_2023_1.iml
View File

@ -1,8 +1,10 @@
<?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$" />
<orderEntry type="jdk" jdkName="Python 3.8 (venv)" jdkType="Python SDK" />
<content url="file://$MODULE_DIR$">
<excludeFolder url="file://$MODULE_DIR$/venv" />
</content>
<orderEntry type="jdk" jdkName="Python 3.9 (PyCharmProjects)" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
</module>

7
.idea/discord.xml Normal file
View File

@ -0,0 +1,7 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="DiscordProjectSettings">
<option name="show" value="ASK" />
<option name="description" value="" />
</component>
</project>

8
.idea/misc.xml
View File

@ -1,4 +1,10 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (venv)" project-jdk-type="Python SDK" />
<component name="Black">
<option name="sdkName" value="Python 3.9 (PyCharmProjects)" />
</component>
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (PyCharmProjects)" project-jdk-type="Python SDK" />
<component name="PyCharmProfessionalAdvertiser">
<option name="shown" value="true" />
</component>
</project>

111
.idea/workspace.xml
View File

@ -1,8 +1,13 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="AutoImportSettings">
<option name="autoReloadType" value="SELECTIVE" />
</component>
<component name="ChangeListManager">
<list default="true" id="0ceb130e-88da-4a20-aad6-17f5ab4226ac" name="Changes" comment="">
<change afterPath="$PROJECT_DIR$/zavrazhnova_svetlana_lab_2/result.png" afterDir="false" />
<change beforePath="$PROJECT_DIR$/.idea/IIS_2023_1.iml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/IIS_2023_1.iml" afterDir="false" />
<change beforePath="$PROJECT_DIR$/.idea/misc.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/misc.xml" afterDir="false" />
<change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
</list>
<option name="SHOW_DIALOG" value="false" />
<option name="HIGHLIGHT_CONFLICTS" value="true" />
@ -17,29 +22,72 @@
</option>
</component>
<component name="Git.Settings">
<option name="RECENT_BRANCH_BY_REPOSITORY">
<map>
<entry key="$PROJECT_DIR$" value="main" />
</map>
</option>
<option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$" />
</component>
<component name="MarkdownSettingsMigration">
<option name="stateVersion" value="1" />
</component>
<component name="ProjectColorInfo">{
&quot;associatedIndex&quot;: 2
}</component>
<component name="ProjectId" id="2VlZqWiOX68aCf0o2y0AtYJWURS" />
<component name="ProjectLevelVcsManager">
<ConfirmationsSetting value="1" id="Add" />
</component>
<component name="ProjectViewState">
<option name="hideEmptyMiddlePackages" value="true" />
<option name="showLibraryContents" value="true" />
</component>
<component name="PropertiesComponent"><![CDATA[{
"keyToString": {
"RunOnceActivity.OpenProjectViewOnStart": "true",
"RunOnceActivity.ShowReadmeOnStart": "true",
"last_opened_file_path": "D:/ulstukek/Course4/IIS/IISLabs/IIS_2023_1"
<component name="PropertiesComponent">{
&quot;keyToString&quot;: {
&quot;RunOnceActivity.OpenProjectViewOnStart&quot;: &quot;true&quot;,
&quot;RunOnceActivity.ShowReadmeOnStart&quot;: &quot;true&quot;,
&quot;WebServerToolWindowFactoryState&quot;: &quot;false&quot;,
&quot;git-widget-placeholder&quot;: &quot;senkin__alexander__lab__1&quot;,
&quot;last_opened_file_path&quot;: &quot;D:/ulstukek/Course4/IIS/labs&quot;,
&quot;node.js.detected.package.eslint&quot;: &quot;true&quot;,
&quot;node.js.detected.package.tslint&quot;: &quot;true&quot;,
&quot;node.js.selected.package.eslint&quot;: &quot;(autodetect)&quot;,
&quot;node.js.selected.package.tslint&quot;: &quot;(autodetect)&quot;,
&quot;nodejs_package_manager_path&quot;: &quot;npm&quot;,
&quot;settings.editor.selected.configurable&quot;: &quot;reference.settings.ide.settings.new.ui&quot;,
&quot;vue.rearranger.settings.migration&quot;: &quot;true&quot;
}
}]]></component>
}</component>
<component name="RecentsManager">
<key name="CopyFile.RECENT_KEYS">
<recent name="D:\ulstukek\Course4\IIS\IISLabs\IIS_2023_1\zavrazhnova_svetlana_lab_3" />
<recent name="D:\ulstukek\Course4\IIS\IISLabs\IIS_2023_1\zavrazhnova_svetlana_lab_1" />
</key>
</component>
<component name="RunManager">
<configuration name="zavrazhnova_svetlana_lab3_2" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
<module name="IIS_2023_1" />
<option name="INTERPRETER_OPTIONS" value="" />
<option name="PARENT_ENVS" value="true" />
<envs>
<env name="PYTHONUNBUFFERED" value="1" />
</envs>
<option name="SDK_HOME" value="" />
<option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/zavrazhnova_svetlana_lab_3" />
<option name="IS_MODULE_SDK" value="true" />
<option name="ADD_CONTENT_ROOTS" value="true" />
<option name="ADD_SOURCE_ROOTS" value="true" />
<EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
<option name="SCRIPT_NAME" value="$PROJECT_DIR$/zavrazhnova_svetlana_lab_3/zavrazhnova_svetlana_lab3_2.py" />
<option name="PARAMETERS" value="" />
<option name="SHOW_COMMAND_LINE" value="false" />
<option name="EMULATE_TERMINAL" value="false" />
<option name="MODULE_MODE" value="false" />
<option name="REDIRECT_INPUT" value="false" />
<option name="INPUT_FILE" value="" />
<method v="2" />
</configuration>
<configuration name="zavrazhnova_svetlana_lab_2" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
<module name="IIS_2023_1" />
<option name="INTERPRETER_OPTIONS" value="" />
@ -52,6 +100,7 @@
<option name="IS_MODULE_SDK" value="true" />
<option name="ADD_CONTENT_ROOTS" value="true" />
<option name="ADD_SOURCE_ROOTS" value="true" />
<EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
<option name="SCRIPT_NAME" value="$PROJECT_DIR$/zavrazhnova_svetlana_lab_2/zavrazhnova_svetlana_lab_2.py" />
<option name="PARAMETERS" value="" />
<option name="SHOW_COMMAND_LINE" value="false" />
@ -61,9 +110,35 @@
<option name="INPUT_FILE" value="" />
<method v="2" />
</configuration>
<configuration name="zavrazhnova_svetlana_lab_3_1" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
<module name="IIS_2023_1" />
<option name="INTERPRETER_OPTIONS" value="" />
<option name="PARENT_ENVS" value="true" />
<envs>
<env name="PYTHONUNBUFFERED" value="1" />
</envs>
<option name="SDK_HOME" value="" />
<option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/zavrazhnova_svetlana_lab_3" />
<option name="IS_MODULE_SDK" value="true" />
<option name="ADD_CONTENT_ROOTS" value="true" />
<option name="ADD_SOURCE_ROOTS" value="true" />
<EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
<option name="SCRIPT_NAME" value="$PROJECT_DIR$/zavrazhnova_svetlana_lab_3/zavrazhnova_svetlana_lab_3_1.py" />
<option name="PARAMETERS" value="" />
<option name="SHOW_COMMAND_LINE" value="false" />
<option name="EMULATE_TERMINAL" value="false" />
<option name="MODULE_MODE" value="false" />
<option name="REDIRECT_INPUT" value="false" />
<option name="INPUT_FILE" value="" />
<method v="2" />
</configuration>
<recent_temporary>
<list>
<item itemvalue="Python.zavrazhnova_svetlana_lab_3_1" />
<item itemvalue="Python.zavrazhnova_svetlana_lab_2" />
<item itemvalue="Python.zavrazhnova_svetlana_lab3_2" />
<item itemvalue="Python.zavrazhnova_svetlana_lab3_2" />
<item itemvalue="Python.zavrazhnova_svetlana_lab_3_1" />
</list>
</recent_temporary>
</component>
@ -75,18 +150,38 @@
<option name="number" value="Default" />
<option name="presentableId" value="Default" />
<updated>1695412818437</updated>
<workItem from="1697735437405" duration="1706000" />
<workItem from="1697740229646" duration="3802000" />
</task>
<servers />
</component>
<component name="TypeScriptGeneratedFilesManager">
<option name="version" value="3" />
</component>
<component name="Vcs.Log.Tabs.Properties">
<option name="TAB_STATES">
<map>
<entry key="MAIN">
<value>
<State />
<State>
<option name="FILTERS">
<map>
<entry key="branch">
<value>
<list>
<option value="HEAD" />
</list>
</value>
</entry>
</map>
</option>
</State>
</value>
</entry>
</map>
</option>
</component>
<component name="com.intellij.coverage.CoverageDataManagerImpl">
<SUITE FILE_PATH="coverage/PyCharmProjects$senkin_alexander_lab_1.coverage" NAME="senkin_alexander_lab_1 Coverage Results" MODIFIED="1697744262965" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/senkin_alexander_lab_1" />
</component>
</project>

47
abanin_daniil_lab_1/README.md Normal file
View File

@ -0,0 +1,47 @@
## Лабораторная работа №1
### Работа с типовыми наборами данных и различными моделями
### ПИбд-41 Абанин Даниил
### Как запустить лабораторную работу:
* установить python, numpy, matplotlib, sklearn
* запустить проект (стартовая точка класс lab1)
### Какие технологии использовались:
* Язык программирования `Python`,
* Библиотеки numpy, matplotlib, sklearn
* Среда разработки `PyCharm`
### Что делает лабораторная работа:
* Программа гененерирует данные с make_moonsmake_moons (noise=0.3, random_state=rs)
* Сравнивает три типа моделей: инейная, полиномиальная, гребневая полиномиальная регрессии
### Примеры работы:
#### Результаты:
MAE - средняя абсолютная ошибка, измеряет среднюю абсолютную разницу между прогнозируемыми значениями модели и фактическими значениями целевой переменной
MSE - средняя квадратическая ошибка, измеряет среднюю квадратичную разницу между прогнозируемыми значениями модели и фактическими значениями целевой переменной
Чем меньше значения показателей, тем лучше модель справляется с предсказанием
Линейная регрессия
MAE 0.2959889435199454
MSE 0.13997968555679302
Полиномиальная регрессия
MAE 0.21662135861071705
MSE 0.08198825629271855
Гребневая полиномиальная регрессия
MAE 0.2102788716636562
MSE 0.07440133949387796
Лучший результат показала модель **Гребневая полиномиальная регрессия**
![Lin](lin_reg.jpg)
![Pol](pol_reg.jpg)
![Greb](greb_reg.jpg)

BIN
abanin_daniil_lab_1/greb_reg.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 59 KiB

66
abanin_daniil_lab_1/lab1.py Normal file
View File

@ -0,0 +1,66 @@
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.datasets import make_moons
from sklearn import metrics
cm_bright = ListedColormap(['#8B0000', '#FF0000'])
cm_bright1 = ListedColormap(['#FF4500', '#FFA500'])
def create_moons():
x, y = make_moons(noise=0.3, random_state=0)
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=.4, random_state=42)
linear_regretion(X_train, X_test, y_train, y_test)
polynomial_regretion(X_train, X_test, y_train, y_test)
ridge_regretion(X_train, X_test, y_train, y_test)
def linear_regretion(x_train, x_test, y_train, y_test):
model = LinearRegression().fit(x_train, y_train)
y_predict = model.intercept_ + model.coef_ * x_test
plt.title('Линейная регрессия')
print('Линейная регрессия')
plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train, cmap=cm_bright)
plt.scatter(x_test[:, 0], x_test[:, 1], c=y_test, cmap=cm_bright1, alpha=0.7)
plt.plot(x_test, y_predict, color='red')
print('MAE', metrics.mean_absolute_error(y_test, y_predict[:, 1]))
print('MSE', metrics.mean_squared_error(y_test, y_predict[:, 1]))
plt.show()
def polynomial_regretion(x_train, x_test, y_train, y_test):
polynomial_features = PolynomialFeatures(degree=3)
X_polynomial = polynomial_features.fit_transform(x_train, y_train)
base_model = LinearRegression()
base_model.fit(X_polynomial, y_train)
y_predict = base_model.predict(X_polynomial)
plt.title('Полиномиальная регрессия')
plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train, cmap=cm_bright)
plt.scatter(x_test[:, 0], x_test[:, 1], c=y_test, cmap=cm_bright1, alpha=0.7)
plt.plot(x_train, y_predict, color='blue')
plt.show()
print('Полиномиальная регрессия')
print('MAE', metrics.mean_absolute_error(y_train, y_predict))
print('MSE', metrics.mean_squared_error(y_train, y_predict))
def ridge_regretion(X_train, X_test, y_train, y_test):
model = Pipeline([('poly', PolynomialFeatures(degree=3)), ('ridge', Ridge(alpha=1.0))])
model.fit(X_train, y_train)
y_predict = model.predict(X_test)
plt.title('Гребневая полиномиальная регрессия')
plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright1, alpha=0.7)
plt.plot(X_test, y_predict, color='blue')
plt.show()
print('Гребневая полиномиальная регрессия')
print('MAE', metrics.mean_absolute_error(y_test, y_predict))
print('MSE', metrics.mean_squared_error(y_test, y_predict))
create_moons()

BIN
abanin_daniil_lab_1/lin_reg.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 30 KiB

BIN
abanin_daniil_lab_1/pol_reg.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 63 KiB

41
abanin_daniil_lab_2/README.md Normal file
View File

@ -0,0 +1,41 @@
## Лабораторная работа №2
### Ранжирование признаков
## ПИбд-41 Абанин Даниил
### Как запустить лабораторную работу:
* установить python, numpy, matplotlib, sklearn
* запустить проект (стартовая точка lab2)
### Какие технологии использовались:
* Язык программирования `Python`, библиотеки numpy, matplotlib, sklearn
* Среда разработки `PyCharm`
### Что делает лабораторная работа:
* Генерирует данные и обучает такие модели, как: LinearRegression, RandomizedLasso, Recursive Feature Elimination (RFE)
* Производиться ранжирование признаков с помощью моделей LinearRegression, RandomizedLasso, Recursive Feature Elimination (RFE)
* Отображение получившихся результатов: 4 самых важных признака по среднему значению, значения признаков для каждой модели
### 4 самых важных признака по среднему значению
* Параметр - x4, значение - 0.56
* Параметр - x1, значение - 0.45
* Параметр - x2, значение - 0.33
* Параметр - x9, значение - 0.33
####Linear Regression
[('x1', 1.0), ('x4', 0.69), ('x2', 0.61), ('x11', 0.59), ('x3', 0.51), ('x13', 0.48), ('x5', 0.19), ('x12', 0.19), ('x14', 0.12), ('x8', 0.03), ('x6', 0.02), ('x10', 0.01), ('x7', 0.0), ('x9', 0.0)]
####Recursive Feature Elimination
[('x9', 1.0), ('x7', 0.86), ('x10', 0.71), ('x6', 0.57), ('x8', 0.43), ('x14', 0.29), ('x12', 0.14), ('x1', 0.0), ('x2', 0.0), ('x3', 0.0), ('x4', 0.0), ('x5', 0.0), ('x11', 0.0), ('x13', 0.0)]
####Randomize Lasso
[('x4', 1.0), ('x2', 0.37), ('x1', 0.36), ('x5', 0.32), ('x6', 0.02), ('x8', 0.02), ('x3', 0.01), ('x7', 0.0), ('x9', 0.0), ('x10', 0.0), ('x11', 0.0), ('x12', 0.0), ('x13', 0.0), ('x14', 0.0)]
#### Результаты:
![Result](result.png)

76
abanin_daniil_lab_2/RadomizedLasso.py Normal file
View File

@ -0,0 +1,76 @@
from sklearn.utils import check_X_y, check_random_state
from sklearn.linear_model import Lasso
from scipy.sparse import issparse
from scipy import sparse
def _rescale_data(x, weights):
if issparse(x):
size = weights.shape[0]
weight_dia = sparse.dia_matrix((1 - weights, 0), (size, size))
x_rescaled = x * weight_dia
else:
x_rescaled = x * (1 - weights)
return x_rescaled
class RandomizedLasso(Lasso):
"""
Randomized version of scikit-learns Lasso class.
Randomized LASSO is a generalization of the LASSO. The LASSO penalises
the absolute value of the coefficients with a penalty term proportional
to `alpha`, but the randomized LASSO changes the penalty to a randomly
chosen value in the range `[alpha, alpha/weakness]`.
Parameters
----------
weakness : float
Weakness value for randomized LASSO. Must be in (0, 1].
See also
--------
sklearn.linear_model.LogisticRegression : learns logistic regression models
using the same algorithm.
"""
def __init__(self, weakness=0.5, alpha=1.0, fit_intercept=True,
precompute=False, copy_X=True, max_iter=1000,
tol=1e-4, warm_start=False, positive=False,
random_state=None, selection='cyclic'):
self.weakness = weakness
super(RandomizedLasso, self).__init__(
alpha=alpha, fit_intercept=fit_intercept, precompute=precompute, copy_X=copy_X,
max_iter=max_iter, tol=tol, warm_start=warm_start,
positive=positive, random_state=random_state,
selection=selection)
def fit(self, X, y):
"""Fit the model according to the given training data.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
The training input samples.
y : array-like, shape = [n_samples]
The target values.
"""
if not isinstance(self.weakness, float) or not (0.0 < self.weakness <= 1.0):
raise ValueError('weakness should be a float in (0, 1], got %s' % self.weakness)
X, y = check_X_y(X, y, accept_sparse=True)
n_features = X.shape[1]
weakness = 1. - self.weakness
random_state = check_random_state(self.random_state)
weights = weakness * random_state.randint(0, 1 + 1, size=(n_features,))
# TODO: I am afraid this will do double normalization if set to true
#X, y, _, _ = _preprocess_data(X, y, self.fit_intercept, normalize=self.normalize, copy=False,
# sample_weight=None, return_mean=False)
# TODO: Check if this is a problem if it happens before standardization
X_rescaled = _rescale_data(X, weights)
return super(RandomizedLasso, self).fit(X_rescaled, y)

BIN
abanin_daniil_lab_2/__pycache__/RadomizedLasso.cpython-39.pyc Normal file
View File

Binary file not shown.

81
abanin_daniil_lab_2/lab2.py Normal file
View File

@ -0,0 +1,81 @@
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
from RadomizedLasso import RandomizedLasso
from sklearn.feature_selection import RFE
from sklearn.preprocessing import MinMaxScaler
import numpy as np
names = ["x%s" % i for i in range(1, 15)]
def start_point():
X,Y = generation_data()
# Линейная модель
lr = LinearRegression()
lr.fit(X, Y)
# Рекурсивное сокращение признаков
rfe = RFE(lr)
rfe.fit(X, Y)
# Случайное Лассо
randomized_lasso = RandomizedLasso(alpha=.01)
randomized_lasso.fit(X, Y)
ranks = {"Linear Regression": rank_to_dict(lr.coef_), "Recursive Feature Elimination": rank_to_dict(rfe.ranking_),
"Randomize Lasso": rank_to_dict(randomized_lasso.coef_)}
get_estimation(ranks)
print_sorted_data(ranks)
def generation_data():
np.random.seed(0)
size = 750
X = np.random.uniform(0, 1, (size, 14))
Y = (10 * np.sin(np.pi * X[:, 0] * X[:, 1]) + 20 * (X[:, 2] - .5) ** 2 +
10 * X[:, 3] + 5 * X[:, 4] ** 5 + np.random.normal(0, 1))
X[:, 10:] = X[:, :4] + np.random.normal(0, .025, (size, 4))
return X, Y
def rank_to_dict(ranks):
ranks = np.abs(ranks)
minmax = MinMaxScaler()
ranks = minmax.fit_transform(np.array(ranks).reshape(14, 1)).ravel()
ranks = map(lambda x: round(x, 2), ranks)
return dict(zip(names, ranks))
def get_estimation(ranks: {}):
mean = {}
#«Бежим» по списку ranks
for key, value in ranks.items():
for item in value.items():
if(item[0] not in mean):
mean[item[0]] = 0
mean[item[0]] += item[1]
for key, value in mean.items():
res = value/len(ranks)
mean[key] = round(res, 2)
mean_sorted = sorted(mean.items(), key=lambda item: item[1], reverse=True)
print("Средние значения")
print(mean_sorted)
print("4 самых важных признака по среднему значению")
for item in mean_sorted[:4]:
print('Параметр - {0}, значение - {1}'.format(item[0], item[1]))
def print_sorted_data(ranks: {}):
print()
for key, value in ranks.items():
ranks[key] = sorted(value.items(), key=lambda item: item[1], reverse=True)
for key, value in ranks.items():
print(key)
print(value)
start_point()

BIN
abanin_daniil_lab_2/result.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 178 KiB

27
abanin_daniil_lab_3/README.md Normal file
View File

@ -0,0 +1,27 @@
## Лабораторная работа №3
### Деревья решений
## Cтудент группы ПИбд-41 Абанин Даниил
### Как запустить лабораторную работу:
* установить python, numpy, matplotlib, sklearn
* запустить проект (lab3)
### Какие технологии использовались:
* Язык программирования `Python`, библиотеки numpy, matplotlib, sklearn
* Среда разработки `PyCharm`
### Что делает лабораторная работа:
* Выполняет ранжирование признаков для регрессионной модели
* По данным "Eligibility Prediction for Loan" решает задачу классификации (с помощью дерева решений), в которой необходимо выявить риски выдачи кредита и определить его статус (выдан или отказ). В качестве исходных данных используются три признака: Credit_History - соответствие кредитной истории стандартам банка, ApplicantIncome - доход заявителя, LoanAmount - сумма кредита.
### Примеры работы:
#### Результаты:
* Наиболее важным параметром при выдачи кредита оказался доход заявителя - ApplicantIncome, затем LoanAmount - сумма выдаваемого кредита
![Result](result.png)

33
abanin_daniil_lab_3/lab3.py Normal file
View File

@ -0,0 +1,33 @@
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import numpy as np
pd.options.mode.chained_assignment = None
FILE_PATH = "loan.csv"
REQUIRED_COLUMNS = ['Credit_History', 'LoanAmount', 'ApplicantIncome']
TARGET_COLUMN = 'Loan_Status'
def print_classifier_info(feature_importance):
feature_names = REQUIRED_COLUMNS
embarked_score = feature_importance[-3:].sum()
scores = np.append(feature_importance[:2], embarked_score)
scores = map(lambda score: round(score, 2), scores)
print(dict(zip(feature_names, scores)))
if __name__ == '__main__':
data = pd.read_csv(FILE_PATH)
X = data[REQUIRED_COLUMNS]
y = data[TARGET_COLUMN]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
classifier_tree = DecisionTreeClassifier(random_state=42)
classifier_tree.fit(X_train, y_train)
print_classifier_info(classifier_tree.feature_importances_)
print("Оценка качества (задача классификации) - ", classifier_tree.score(X_test, y_test))

615
abanin_daniil_lab_3/loan.csv Normal file
View File

@ -0,0 +1,615 @@
Loan_ID,Gender,Married,Dependents,Education,Self_Employed,ApplicantIncome,CoapplicantIncome,LoanAmount,Loan_Amount_Term,Credit_History,Property_Area,Loan_Status
LP001002,Male,No,0,1,No,5849,0.0,360.0,1.0,0,Y,0.0
LP001003,Male,Yes,1,1,No,4583,1508.0,128.0,360.0,1,Rural,0.0
LP001005,Male,Yes,0,1,Yes,3000,0.0,66.0,360.0,1,Urban,1.0
LP001006,Male,Yes,0,0,No,2583,2358.0,120.0,360.0,1,Urban,1.0
LP001008,Male,No,0,1,No,6000,0.0,141.0,360.0,1,Urban,1.0
LP001011,Male,Yes,2,1,Yes,5417,4196.0,267.0,360.0,1,Urban,1.0
LP001013,Male,Yes,0,0,No,2333,1516.0,95.0,360.0,1,Urban,1.0
LP001014,Male,Yes,3+,1,No,3036,2504.0,158.0,360.0,0,Semiurban,0.0
LP001018,Male,Yes,2,1,No,4006,1526.0,168.0,360.0,1,Urban,1.0
LP001020,Male,Yes,1,1,No,12841,10968.0,349.0,360.0,1,Semiurban,0.0
LP001024,Male,Yes,2,1,No,3200,700.0,70.0,360.0,1,Urban,1.0
LP001027,Male,Yes,2,1,,2500,1840.0,109.0,360.0,1,Urban,1.0
LP001028,Male,Yes,2,1,No,3073,8106.0,200.0,360.0,1,Urban,1.0
LP001029,Male,No,0,1,No,1853,2840.0,114.0,360.0,1,Rural,0.0
LP001030,Male,Yes,2,1,No,1299,1086.0,17.0,120.0,1,Urban,1.0
LP001032,Male,No,0,1,No,4950,0.0,125.0,360.0,1,Urban,1.0
LP001034,Male,No,1,0,No,3596,0.0,100.0,240.0,0,Urban,1.0
LP001036,Female,No,0,1,No,3510,0.0,76.0,360.0,0,Urban,0.0
LP001038,Male,Yes,0,0,No,4887,0.0,133.0,360.0,1,Rural,0.0
LP001041,Male,Yes,0,1,,2600,3500.0,115.0,,1,Urban,1.0
LP001043,Male,Yes,0,0,No,7660,0.0,104.0,360.0,0,Urban,0.0
LP001046,Male,Yes,1,1,No,5955,5625.0,315.0,360.0,1,Urban,1.0
LP001047,Male,Yes,0,0,No,2600,1911.0,116.0,360.0,0,Semiurban,0.0
LP001050,,Yes,2,0,No,3365,1917.0,112.0,360.0,0,Rural,0.0
LP001052,Male,Yes,1,1,,3717,2925.0,151.0,360.0,0,Semiurban,0.0
LP001066,Male,Yes,0,1,Yes,9560,0.0,191.0,360.0,1,Semiurban,1.0
LP001068,Male,Yes,0,1,No,2799,2253.0,122.0,360.0,1,Semiurban,1.0
LP001073,Male,Yes,2,0,No,4226,1040.0,110.0,360.0,1,Urban,1.0
LP001086,Male,No,0,0,No,1442,0.0,35.0,360.0,1,Urban,0.0
LP001087,Female,No,2,1,,3750,2083.0,120.0,360.0,1,Semiurban,1.0
LP001091,Male,Yes,1,1,,4166,3369.0,201.0,360.0,0,Urban,0.0
LP001095,Male,No,0,1,No,3167,0.0,74.0,360.0,1,Urban,0.0
LP001097,Male,No,1,1,Yes,4692,0.0,106.0,360.0,1,Rural,0.0
LP001098,Male,Yes,0,1,No,3500,1667.0,114.0,360.0,1,Semiurban,1.0
LP001100,Male,No,3+,1,No,12500,3000.0,320.0,360.0,1,Rural,0.0
LP001106,Male,Yes,0,1,No,2275,2067.0,0.0,360.0,1,Urban,1.0
LP001109,Male,Yes,0,1,No,1828,1330.0,100.0,,0,Urban,0.0
LP001112,Female,Yes,0,1,No,3667,1459.0,144.0,360.0,1,Semiurban,1.0
LP001114,Male,No,0,1,No,4166,7210.0,184.0,360.0,1,Urban,1.0
LP001116,Male,No,0,0,No,3748,1668.0,110.0,360.0,1,Semiurban,1.0
LP001119,Male,No,0,1,No,3600,0.0,80.0,360.0,1,Urban,0.0
LP001120,Male,No,0,1,No,1800,1213.0,47.0,360.0,1,Urban,1.0
LP001123,Male,Yes,0,1,No,2400,0.0,75.0,360.0,0,Urban,1.0
LP001131,Male,Yes,0,1,No,3941,2336.0,134.0,360.0,1,Semiurban,1.0
LP001136,Male,Yes,0,0,Yes,4695,0.0,96.0,,1,Urban,1.0
LP001137,Female,No,0,1,No,3410,0.0,88.0,,1,Urban,1.0
LP001138,Male,Yes,1,1,No,5649,0.0,44.0,360.0,1,Urban,1.0
LP001144,Male,Yes,0,1,No,5821,0.0,144.0,360.0,1,Urban,1.0
LP001146,Female,Yes,0,1,No,2645,3440.0,120.0,360.0,0,Urban,0.0
LP001151,Female,No,0,1,No,4000,2275.0,144.0,360.0,1,Semiurban,1.0
LP001155,Female,Yes,0,0,No,1928,1644.0,100.0,360.0,1,Semiurban,1.0
LP001157,Female,No,0,1,No,3086,0.0,120.0,360.0,1,Semiurban,1.0
LP001164,Female,No,0,1,No,4230,0.0,112.0,360.0,1,Semiurban,0.0
LP001179,Male,Yes,2,1,No,4616,0.0,134.0,360.0,1,Urban,0.0
LP001186,Female,Yes,1,1,Yes,11500,0.0,286.0,360.0,0,Urban,0.0
LP001194,Male,Yes,2,1,No,2708,1167.0,97.0,360.0,1,Semiurban,1.0
LP001195,Male,Yes,0,1,No,2132,1591.0,96.0,360.0,1,Semiurban,1.0
LP001197,Male,Yes,0,1,No,3366,2200.0,135.0,360.0,1,Rural,0.0
LP001198,Male,Yes,1,1,No,8080,2250.0,180.0,360.0,1,Urban,1.0
LP001199,Male,Yes,2,0,No,3357,2859.0,144.0,360.0,1,Urban,1.0
LP001205,Male,Yes,0,1,No,2500,3796.0,120.0,360.0,1,Urban,1.0
LP001206,Male,Yes,3+,1,No,3029,0.0,99.0,360.0,1,Urban,1.0
LP001207,Male,Yes,0,0,Yes,2609,3449.0,165.0,180.0,0,Rural,0.0
LP001213,Male,Yes,1,1,No,4945,0.0,0.0,360.0,0,Rural,0.0
LP001222,Female,No,0,1,No,4166,0.0,116.0,360.0,0,Semiurban,0.0
LP001225,Male,Yes,0,1,No,5726,4595.0,258.0,360.0,1,Semiurban,0.0
LP001228,Male,No,0,0,No,3200,2254.0,126.0,180.0,0,Urban,0.0
LP001233,Male,Yes,1,1,No,10750,0.0,312.0,360.0,1,Urban,1.0
LP001238,Male,Yes,3+,0,Yes,7100,0.0,125.0,60.0,1,Urban,1.0
LP001241,Female,No,0,1,No,4300,0.0,136.0,360.0,0,Semiurban,0.0
LP001243,Male,Yes,0,1,No,3208,3066.0,172.0,360.0,1,Urban,1.0
LP001245,Male,Yes,2,0,Yes,1875,1875.0,97.0,360.0,1,Semiurban,1.0
LP001248,Male,No,0,1,No,3500,0.0,81.0,300.0,1,Semiurban,1.0
LP001250,Male,Yes,3+,0,No,4755,0.0,95.0,,0,Semiurban,0.0
LP001253,Male,Yes,3+,1,Yes,5266,1774.0,187.0,360.0,1,Semiurban,1.0
LP001255,Male,No,0,1,No,3750,0.0,113.0,480.0,1,Urban,0.0
LP001256,Male,No,0,1,No,3750,4750.0,176.0,360.0,1,Urban,0.0
LP001259,Male,Yes,1,1,Yes,1000,3022.0,110.0,360.0,1,Urban,0.0
LP001263,Male,Yes,3+,1,No,3167,4000.0,180.0,300.0,0,Semiurban,0.0
LP001264,Male,Yes,3+,0,Yes,3333,2166.0,130.0,360.0,0,Semiurban,1.0
LP001265,Female,No,0,1,No,3846,0.0,111.0,360.0,1,Semiurban,1.0
LP001266,Male,Yes,1,1,Yes,2395,0.0,0.0,360.0,1,Semiurban,1.0
LP001267,Female,Yes,2,1,No,1378,1881.0,167.0,360.0,1,Urban,0.0
LP001273,Male,Yes,0,1,No,6000,2250.0,265.0,360.0,0,Semiurban,0.0
LP001275,Male,Yes,1,1,No,3988,0.0,50.0,240.0,1,Urban,1.0
LP001279,Male,No,0,1,No,2366,2531.0,136.0,360.0,1,Semiurban,1.0
LP001280,Male,Yes,2,0,No,3333,2000.0,99.0,360.0,0,Semiurban,1.0
LP001282,Male,Yes,0,1,No,2500,2118.0,104.0,360.0,1,Semiurban,1.0
LP001289,Male,No,0,1,No,8566,0.0,210.0,360.0,1,Urban,1.0
LP001310,Male,Yes,0,1,No,5695,4167.0,175.0,360.0,1,Semiurban,1.0
LP001316,Male,Yes,0,1,No,2958,2900.0,131.0,360.0,1,Semiurban,1.0
LP001318,Male,Yes,2,1,No,6250,5654.0,188.0,180.0,1,Semiurban,1.0
LP001319,Male,Yes,2,0,No,3273,1820.0,81.0,360.0,1,Urban,1.0
LP001322,Male,No,0,1,No,4133,0.0,122.0,360.0,1,Semiurban,1.0
LP001325,Male,No,0,0,No,3620,0.0,25.0,120.0,1,Semiurban,1.0
LP001326,Male,No,0,1,,6782,0.0,0.0,360.0,0,Urban,0.0
LP001327,Female,Yes,0,1,No,2484,2302.0,137.0,360.0,1,Semiurban,1.0
LP001333,Male,Yes,0,1,No,1977,997.0,50.0,360.0,1,Semiurban,1.0
LP001334,Male,Yes,0,0,No,4188,0.0,115.0,180.0,1,Semiurban,1.0
LP001343,Male,Yes,0,1,No,1759,3541.0,131.0,360.0,1,Semiurban,1.0
LP001345,Male,Yes,2,0,No,4288,3263.0,133.0,180.0,1,Urban,1.0
LP001349,Male,No,0,1,No,4843,3806.0,151.0,360.0,1,Semiurban,1.0
LP001350,Male,Yes,,1,No,13650,0.0,0.0,360.0,1,Urban,1.0
LP001356,Male,Yes,0,1,No,4652,3583.0,0.0,360.0,1,Semiurban,1.0
LP001357,Male,,,1,No,3816,754.0,160.0,360.0,1,Urban,1.0
LP001367,Male,Yes,1,1,No,3052,1030.0,100.0,360.0,1,Urban,1.0
LP001369,Male,Yes,2,1,No,11417,1126.0,225.0,360.0,1,Urban,1.0
LP001370,Male,No,0,0,,7333,0.0,120.0,360.0,1,Rural,0.0
LP001379,Male,Yes,2,1,No,3800,3600.0,216.0,360.0,0,Urban,0.0
LP001384,Male,Yes,3+,0,No,2071,754.0,94.0,480.0,1,Semiurban,1.0
LP001385,Male,No,0,1,No,5316,0.0,136.0,360.0,1,Urban,1.0
LP001387,Female,Yes,0,1,,2929,2333.0,139.0,360.0,1,Semiurban,1.0
LP001391,Male,Yes,0,0,No,3572,4114.0,152.0,,0,Rural,0.0
LP001392,Female,No,1,1,Yes,7451,0.0,0.0,360.0,1,Semiurban,1.0
LP001398,Male,No,0,1,,5050,0.0,118.0,360.0,1,Semiurban,1.0
LP001401,Male,Yes,1,1,No,14583,0.0,185.0,180.0,1,Rural,1.0
LP001404,Female,Yes,0,1,No,3167,2283.0,154.0,360.0,1,Semiurban,1.0
LP001405,Male,Yes,1,1,No,2214,1398.0,85.0,360.0,0,Urban,1.0
LP001421,Male,Yes,0,1,No,5568,2142.0,175.0,360.0,1,Rural,0.0
LP001422,Female,No,0,1,No,10408,0.0,259.0,360.0,1,Urban,1.0
LP001426,Male,Yes,,1,No,5667,2667.0,180.0,360.0,1,Rural,1.0
LP001430,Female,No,0,1,No,4166,0.0,44.0,360.0,1,Semiurban,1.0
LP001431,Female,No,0,1,No,2137,8980.0,137.0,360.0,0,Semiurban,1.0
LP001432,Male,Yes,2,1,No,2957,0.0,81.0,360.0,1,Semiurban,1.0
LP001439,Male,Yes,0,0,No,4300,2014.0,194.0,360.0,1,Rural,1.0
LP001443,Female,No,0,1,No,3692,0.0,93.0,360.0,0,Rural,1.0
LP001448,,Yes,3+,1,No,23803,0.0,370.0,360.0,1,Rural,1.0
LP001449,Male,No,0,1,No,3865,1640.0,0.0,360.0,1,Rural,1.0
LP001451,Male,Yes,1,1,Yes,10513,3850.0,160.0,180.0,0,Urban,0.0
LP001465,Male,Yes,0,1,No,6080,2569.0,182.0,360.0,0,Rural,0.0
LP001469,Male,No,0,1,Yes,20166,0.0,650.0,480.0,0,Urban,1.0
LP001473,Male,No,0,1,No,2014,1929.0,74.0,360.0,1,Urban,1.0
LP001478,Male,No,0,1,No,2718,0.0,70.0,360.0,1,Semiurban,1.0
LP001482,Male,Yes,0,1,Yes,3459,0.0,25.0,120.0,1,Semiurban,1.0
LP001487,Male,No,0,1,No,4895,0.0,102.0,360.0,1,Semiurban,1.0
LP001488,Male,Yes,3+,1,No,4000,7750.0,290.0,360.0,1,Semiurban,0.0
LP001489,Female,Yes,0,1,No,4583,0.0,84.0,360.0,1,Rural,0.0
LP001491,Male,Yes,2,1,Yes,3316,3500.0,88.0,360.0,1,Urban,1.0
LP001492,Male,No,0,1,No,14999,0.0,242.0,360.0,0,Semiurban,0.0
LP001493,Male,Yes,2,0,No,4200,1430.0,129.0,360.0,1,Rural,0.0
LP001497,Male,Yes,2,1,No,5042,2083.0,185.0,360.0,1,Rural,0.0
LP001498,Male,No,0,1,No,5417,0.0,168.0,360.0,1,Urban,1.0
LP001504,Male,No,0,1,Yes,6950,0.0,175.0,180.0,1,Semiurban,1.0
LP001507,Male,Yes,0,1,No,2698,2034.0,122.0,360.0,1,Semiurban,1.0
LP001508,Male,Yes,2,1,No,11757,0.0,187.0,180.0,1,Urban,1.0
LP001514,Female,Yes,0,1,No,2330,4486.0,100.0,360.0,1,Semiurban,1.0
LP001516,Female,Yes,2,1,No,14866,0.0,70.0,360.0,1,Urban,1.0
LP001518,Male,Yes,1,1,No,1538,1425.0,30.0,360.0,1,Urban,1.0
LP001519,Female,No,0,1,No,10000,1666.0,225.0,360.0,1,Rural,0.0
LP001520,Male,Yes,0,1,No,4860,830.0,125.0,360.0,1,Semiurban,1.0
LP001528,Male,No,0,1,No,6277,0.0,118.0,360.0,0,Rural,0.0
LP001529,Male,Yes,0,1,Yes,2577,3750.0,152.0,360.0,1,Rural,1.0
LP001531,Male,No,0,1,No,9166,0.0,244.0,360.0,1,Urban,0.0
LP001532,Male,Yes,2,0,No,2281,0.0,113.0,360.0,1,Rural,0.0
LP001535,Male,No,0,1,No,3254,0.0,50.0,360.0,1,Urban,1.0
LP001536,Male,Yes,3+,1,No,39999,0.0,600.0,180.0,0,Semiurban,1.0
LP001541,Male,Yes,1,1,No,6000,0.0,160.0,360.0,0,Rural,1.0
LP001543,Male,Yes,1,1,No,9538,0.0,187.0,360.0,1,Urban,1.0
LP001546,Male,No,0,1,,2980,2083.0,120.0,360.0,1,Rural,1.0
LP001552,Male,Yes,0,1,No,4583,5625.0,255.0,360.0,1,Semiurban,1.0
LP001560,Male,Yes,0,0,No,1863,1041.0,98.0,360.0,1,Semiurban,1.0
LP001562,Male,Yes,0,1,No,7933,0.0,275.0,360.0,1,Urban,0.0
LP001565,Male,Yes,1,1,No,3089,1280.0,121.0,360.0,0,Semiurban,0.0
LP001570,Male,Yes,2,1,No,4167,1447.0,158.0,360.0,1,Rural,1.0
LP001572,Male,Yes,0,1,No,9323,0.0,75.0,180.0,1,Urban,1.0
LP001574,Male,Yes,0,1,No,3707,3166.0,182.0,,1,Rural,1.0
LP001577,Female,Yes,0,1,No,4583,0.0,112.0,360.0,1,Rural,0.0
LP001578,Male,Yes,0,1,No,2439,3333.0,129.0,360.0,1,Rural,1.0
LP001579,Male,No,0,1,No,2237,0.0,63.0,480.0,0,Semiurban,0.0
LP001580,Male,Yes,2,1,No,8000,0.0,200.0,360.0,1,Semiurban,1.0
LP001581,Male,Yes,0,0,,1820,1769.0,95.0,360.0,1,Rural,1.0
LP001585,,Yes,3+,1,No,51763,0.0,700.0,300.0,1,Urban,1.0
LP001586,Male,Yes,3+,0,No,3522,0.0,81.0,180.0,1,Rural,0.0
LP001594,Male,Yes,0,1,No,5708,5625.0,187.0,360.0,1,Semiurban,1.0
LP001603,Male,Yes,0,0,Yes,4344,736.0,87.0,360.0,1,Semiurban,0.0
LP001606,Male,Yes,0,1,No,3497,1964.0,116.0,360.0,1,Rural,1.0
LP001608,Male,Yes,2,1,No,2045,1619.0,101.0,360.0,1,Rural,1.0
LP001610,Male,Yes,3+,1,No,5516,11300.0,495.0,360.0,0,Semiurban,0.0
LP001616,Male,Yes,1,1,No,3750,0.0,116.0,360.0,1,Semiurban,1.0
LP001630,Male,No,0,0,No,2333,1451.0,102.0,480.0,0,Urban,0.0
LP001633,Male,Yes,1,1,No,6400,7250.0,180.0,360.0,0,Urban,0.0
LP001634,Male,No,0,1,No,1916,5063.0,67.0,360.0,0,Rural,0.0
LP001636,Male,Yes,0,1,No,4600,0.0,73.0,180.0,1,Semiurban,1.0
LP001637,Male,Yes,1,1,No,33846,0.0,260.0,360.0,1,Semiurban,0.0
LP001639,Female,Yes,0,1,No,3625,0.0,108.0,360.0,1,Semiurban,1.0
LP001640,Male,Yes,0,1,Yes,39147,4750.0,120.0,360.0,1,Semiurban,1.0
LP001641,Male,Yes,1,1,Yes,2178,0.0,66.0,300.0,0,Rural,0.0
LP001643,Male,Yes,0,1,No,2383,2138.0,58.0,360.0,0,Rural,1.0
LP001644,,Yes,0,1,Yes,674,5296.0,168.0,360.0,1,Rural,1.0
LP001647,Male,Yes,0,1,No,9328,0.0,188.0,180.0,1,Rural,1.0
LP001653,Male,No,0,0,No,4885,0.0,48.0,360.0,1,Rural,1.0
LP001656,Male,No,0,1,No,12000,0.0,164.0,360.0,1,Semiurban,0.0
LP001657,Male,Yes,0,0,No,6033,0.0,160.0,360.0,1,Urban,0.0
LP001658,Male,No,0,1,No,3858,0.0,76.0,360.0,1,Semiurban,1.0
LP001664,Male,No,0,1,No,4191,0.0,120.0,360.0,1,Rural,1.0
LP001665,Male,Yes,1,1,No,3125,2583.0,170.0,360.0,1,Semiurban,0.0
LP001666,Male,No,0,1,No,8333,3750.0,187.0,360.0,1,Rural,1.0
LP001669,Female,No,0,0,No,1907,2365.0,120.0,,1,Urban,1.0
LP001671,Female,Yes,0,1,No,3416,2816.0,113.0,360.0,0,Semiurban,1.0
LP001673,Male,No,0,1,Yes,11000,0.0,83.0,360.0,1,Urban,0.0
LP001674,Male,Yes,1,0,No,2600,2500.0,90.0,360.0,1,Semiurban,1.0
LP001677,Male,No,2,1,No,4923,0.0,166.0,360.0,0,Semiurban,1.0
LP001682,Male,Yes,3+,0,No,3992,0.0,0.0,180.0,1,Urban,0.0
LP001688,Male,Yes,1,0,No,3500,1083.0,135.0,360.0,1,Urban,1.0
LP001691,Male,Yes,2,0,No,3917,0.0,124.0,360.0,1,Semiurban,1.0
LP001692,Female,No,0,0,No,4408,0.0,120.0,360.0,1,Semiurban,1.0
LP001693,Female,No,0,1,No,3244,0.0,80.0,360.0,1,Urban,1.0
LP001698,Male,No,0,0,No,3975,2531.0,55.0,360.0,1,Rural,1.0
LP001699,Male,No,0,1,No,2479,0.0,59.0,360.0,1,Urban,1.0
LP001702,Male,No,0,1,No,3418,0.0,127.0,360.0,1,Semiurban,0.0
LP001708,Female,No,0,1,No,10000,0.0,214.0,360.0,1,Semiurban,0.0
LP001711,Male,Yes,3+,1,No,3430,1250.0,128.0,360.0,0,Semiurban,0.0
LP001713,Male,Yes,1,1,Yes,7787,0.0,240.0,360.0,1,Urban,1.0
LP001715,Male,Yes,3+,0,Yes,5703,0.0,130.0,360.0,1,Rural,1.0
LP001716,Male,Yes,0,1,No,3173,3021.0,137.0,360.0,1,Urban,1.0
LP001720,Male,Yes,3+,0,No,3850,983.0,100.0,360.0,1,Semiurban,1.0
LP001722,Male,Yes,0,1,No,150,1800.0,135.0,360.0,1,Rural,0.0
LP001726,Male,Yes,0,1,No,3727,1775.0,131.0,360.0,1,Semiurban,1.0
LP001732,Male,Yes,2,1,,5000,0.0,72.0,360.0,0,Semiurban,0.0
LP001734,Female,Yes,2,1,No,4283,2383.0,127.0,360.0,0,Semiurban,1.0
LP001736,Male,Yes,0,1,No,2221,0.0,60.0,360.0,0,Urban,0.0
LP001743,Male,Yes,2,1,No,4009,1717.0,116.0,360.0,1,Semiurban,1.0
LP001744,Male,No,0,1,No,2971,2791.0,144.0,360.0,1,Semiurban,1.0
LP001749,Male,Yes,0,1,No,7578,1010.0,175.0,,1,Semiurban,1.0
LP001750,Male,Yes,0,1,No,6250,0.0,128.0,360.0,1,Semiurban,1.0
LP001751,Male,Yes,0,1,No,3250,0.0,170.0,360.0,1,Rural,0.0
LP001754,Male,Yes,,0,Yes,4735,0.0,138.0,360.0,1,Urban,0.0
LP001758,Male,Yes,2,1,No,6250,1695.0,210.0,360.0,1,Semiurban,1.0
LP001760,Male,,,1,No,4758,0.0,158.0,480.0,1,Semiurban,1.0
LP001761,Male,No,0,1,Yes,6400,0.0,200.0,360.0,1,Rural,1.0
LP001765,Male,Yes,1,1,No,2491,2054.0,104.0,360.0,1,Semiurban,1.0
LP001768,Male,Yes,0,1,,3716,0.0,42.0,180.0,1,Rural,1.0
LP001770,Male,No,0,0,No,3189,2598.0,120.0,,1,Rural,1.0
LP001776,Female,No,0,1,No,8333,0.0,280.0,360.0,1,Semiurban,1.0
LP001778,Male,Yes,1,1,No,3155,1779.0,140.0,360.0,1,Semiurban,1.0
LP001784,Male,Yes,1,1,No,5500,1260.0,170.0,360.0,1,Rural,1.0
LP001786,Male,Yes,0,1,,5746,0.0,255.0,360.0,0,Urban,0.0
LP001788,Female,No,0,1,Yes,3463,0.0,122.0,360.0,0,Urban,1.0
LP001790,Female,No,1,1,No,3812,0.0,112.0,360.0,1,Rural,1.0
LP001792,Male,Yes,1,1,No,3315,0.0,96.0,360.0,1,Semiurban,1.0
LP001798,Male,Yes,2,1,No,5819,5000.0,120.0,360.0,1,Rural,1.0
LP001800,Male,Yes,1,0,No,2510,1983.0,140.0,180.0,1,Urban,0.0
LP001806,Male,No,0,1,No,2965,5701.0,155.0,60.0,1,Urban,1.0
LP001807,Male,Yes,2,1,Yes,6250,1300.0,108.0,360.0,1,Rural,1.0
LP001811,Male,Yes,0,0,No,3406,4417.0,123.0,360.0,1,Semiurban,1.0
LP001813,Male,No,0,1,Yes,6050,4333.0,120.0,180.0,1,Urban,0.0
LP001814,Male,Yes,2,1,No,9703,0.0,112.0,360.0,1,Urban,1.0
LP001819,Male,Yes,1,0,No,6608,0.0,137.0,180.0,1,Urban,1.0
LP001824,Male,Yes,1,1,No,2882,1843.0,123.0,480.0,1,Semiurban,1.0
LP001825,Male,Yes,0,1,No,1809,1868.0,90.0,360.0,1,Urban,1.0
LP001835,Male,Yes,0,0,No,1668,3890.0,201.0,360.0,0,Semiurban,0.0
LP001836,Female,No,2,1,No,3427,0.0,138.0,360.0,1,Urban,0.0
LP001841,Male,No,0,0,Yes,2583,2167.0,104.0,360.0,1,Rural,1.0
LP001843,Male,Yes,1,0,No,2661,7101.0,279.0,180.0,1,Semiurban,1.0
LP001844,Male,No,0,1,Yes,16250,0.0,192.0,360.0,0,Urban,0.0
LP001846,Female,No,3+,1,No,3083,0.0,255.0,360.0,1,Rural,1.0
LP001849,Male,No,0,0,No,6045,0.0,115.0,360.0,0,Rural,0.0
LP001854,Male,Yes,3+,1,No,5250,0.0,94.0,360.0,1,Urban,0.0
LP001859,Male,Yes,0,1,No,14683,2100.0,304.0,360.0,1,Rural,0.0
LP001864,Male,Yes,3+,0,No,4931,0.0,128.0,360.0,0,Semiurban,0.0
LP001865,Male,Yes,1,1,No,6083,4250.0,330.0,360.0,0,Urban,1.0
LP001868,Male,No,0,1,No,2060,2209.0,134.0,360.0,1,Semiurban,1.0
LP001870,Female,No,1,1,No,3481,0.0,155.0,36.0,1,Semiurban,0.0
LP001871,Female,No,0,1,No,7200,0.0,120.0,360.0,1,Rural,1.0
LP001872,Male,No,0,1,Yes,5166,0.0,128.0,360.0,1,Semiurban,1.0
LP001875,Male,No,0,1,No,4095,3447.0,151.0,360.0,1,Rural,1.0
LP001877,Male,Yes,2,1,No,4708,1387.0,150.0,360.0,1,Semiurban,1.0
LP001882,Male,Yes,3+,1,No,4333,1811.0,160.0,360.0,0,Urban,1.0
LP001883,Female,No,0,1,,3418,0.0,135.0,360.0,1,Rural,0.0
LP001884,Female,No,1,1,No,2876,1560.0,90.0,360.0,1,Urban,1.0
LP001888,Female,No,0,1,No,3237,0.0,30.0,360.0,1,Urban,1.0
LP001891,Male,Yes,0,1,No,11146,0.0,136.0,360.0,1,Urban,1.0
LP001892,Male,No,0,1,No,2833,1857.0,126.0,360.0,1,Rural,1.0
LP001894,Male,Yes,0,1,No,2620,2223.0,150.0,360.0,1,Semiurban,1.0
LP001896,Male,Yes,2,1,No,3900,0.0,90.0,360.0,1,Semiurban,1.0
LP001900,Male,Yes,1,1,No,2750,1842.0,115.0,360.0,1,Semiurban,1.0
LP001903,Male,Yes,0,1,No,3993,3274.0,207.0,360.0,1,Semiurban,1.0
LP001904,Male,Yes,0,1,No,3103,1300.0,80.0,360.0,1,Urban,1.0
LP001907,Male,Yes,0,1,No,14583,0.0,436.0,360.0,1,Semiurban,1.0
LP001908,Female,Yes,0,0,No,4100,0.0,124.0,360.0,0,Rural,1.0
LP001910,Male,No,1,0,Yes,4053,2426.0,158.0,360.0,0,Urban,0.0
LP001914,Male,Yes,0,1,No,3927,800.0,112.0,360.0,1,Semiurban,1.0
LP001915,Male,Yes,2,1,No,2301,985.7999878,78.0,180.0,1,Urban,1.0
LP001917,Female,No,0,1,No,1811,1666.0,54.0,360.0,1,Urban,1.0
LP001922,Male,Yes,0,1,No,20667,0.0,0.0,360.0,1,Rural,0.0
LP001924,Male,No,0,1,No,3158,3053.0,89.0,360.0,1,Rural,1.0
LP001925,Female,No,0,1,Yes,2600,1717.0,99.0,300.0,1,Semiurban,0.0
LP001926,Male,Yes,0,1,No,3704,2000.0,120.0,360.0,1,Rural,1.0
LP001931,Female,No,0,1,No,4124,0.0,115.0,360.0,1,Semiurban,1.0
LP001935,Male,No,0,1,No,9508,0.0,187.0,360.0,1,Rural,1.0
LP001936,Male,Yes,0,1,No,3075,2416.0,139.0,360.0,1,Rural,1.0
LP001938,Male,Yes,2,1,No,4400,0.0,127.0,360.0,0,Semiurban,0.0
LP001940,Male,Yes,2,1,No,3153,1560.0,134.0,360.0,1,Urban,1.0
LP001945,Female,No,,1,No,5417,0.0,143.0,480.0,0,Urban,0.0
LP001947,Male,Yes,0,1,No,2383,3334.0,172.0,360.0,1,Semiurban,1.0
LP001949,Male,Yes,3+,1,,4416,1250.0,110.0,360.0,1,Urban,1.0
LP001953,Male,Yes,1,1,No,6875,0.0,200.0,360.0,1,Semiurban,1.0
LP001954,Female,Yes,1,1,No,4666,0.0,135.0,360.0,1,Urban,1.0
LP001955,Female,No,0,1,No,5000,2541.0,151.0,480.0,1,Rural,0.0
LP001963,Male,Yes,1,1,No,2014,2925.0,113.0,360.0,1,Urban,0.0
LP001964,Male,Yes,0,0,No,1800,2934.0,93.0,360.0,0,Urban,0.0
LP001972,Male,Yes,,0,No,2875,1750.0,105.0,360.0,1,Semiurban,1.0
LP001974,Female,No,0,1,No,5000,0.0,132.0,360.0,1,Rural,1.0
LP001977,Male,Yes,1,1,No,1625,1803.0,96.0,360.0,1,Urban,1.0
LP001978,Male,No,0,1,No,4000,2500.0,140.0,360.0,1,Rural,1.0
LP001990,Male,No,0,0,No,2000,0.0,0.0,360.0,1,Urban,0.0
LP001993,Female,No,0,1,No,3762,1666.0,135.0,360.0,1,Rural,1.0
LP001994,Female,No,0,1,No,2400,1863.0,104.0,360.0,0,Urban,0.0
LP001996,Male,No,0,1,No,20233,0.0,480.0,360.0,1,Rural,0.0
LP001998,Male,Yes,2,0,No,7667,0.0,185.0,360.0,0,Rural,1.0
LP002002,Female,No,0,1,No,2917,0.0,84.0,360.0,1,Semiurban,1.0
LP002004,Male,No,0,0,No,2927,2405.0,111.0,360.0,1,Semiurban,1.0
LP002006,Female,No,0,1,No,2507,0.0,56.0,360.0,1,Rural,1.0
LP002008,Male,Yes,2,1,Yes,5746,0.0,144.0,84.0,0,Rural,1.0
LP002024,,Yes,0,1,No,2473,1843.0,159.0,360.0,1,Rural,0.0
LP002031,Male,Yes,1,0,No,3399,1640.0,111.0,180.0,1,Urban,1.0
LP002035,Male,Yes,2,1,No,3717,0.0,120.0,360.0,1,Semiurban,1.0
LP002036,Male,Yes,0,1,No,2058,2134.0,88.0,360.0,0,Urban,1.0
LP002043,Female,No,1,1,No,3541,0.0,112.0,360.0,0,Semiurban,1.0
LP002050,Male,Yes,1,1,Yes,10000,0.0,155.0,360.0,1,Rural,0.0
LP002051,Male,Yes,0,1,No,2400,2167.0,115.0,360.0,1,Semiurban,1.0
LP002053,Male,Yes,3+,1,No,4342,189.0,124.0,360.0,1,Semiurban,1.0
LP002054,Male,Yes,2,0,No,3601,1590.0,0.0,360.0,1,Rural,1.0
LP002055,Female,No,0,1,No,3166,2985.0,132.0,360.0,0,Rural,1.0
LP002065,Male,Yes,3+,1,No,15000,0.0,300.0,360.0,1,Rural,1.0
LP002067,Male,Yes,1,1,Yes,8666,4983.0,376.0,360.0,0,Rural,0.0
LP002068,Male,No,0,1,No,4917,0.0,130.0,360.0,0,Rural,1.0
LP002082,Male,Yes,0,1,Yes,5818,2160.0,184.0,360.0,1,Semiurban,1.0
LP002086,Female,Yes,0,1,No,4333,2451.0,110.0,360.0,1,Urban,0.0
LP002087,Female,No,0,1,No,2500,0.0,67.0,360.0,1,Urban,1.0
LP002097,Male,No,1,1,No,4384,1793.0,117.0,360.0,1,Urban,1.0
LP002098,Male,No,0,1,No,2935,0.0,98.0,360.0,1,Semiurban,1.0
LP002100,Male,No,,1,No,2833,0.0,71.0,360.0,1,Urban,1.0
LP002101,Male,Yes,0,1,,63337,0.0,490.0,180.0,1,Urban,1.0
LP002103,,Yes,1,1,Yes,9833,1833.0,182.0,180.0,1,Urban,1.0
LP002106,Male,Yes,,1,Yes,5503,4490.0,70.0,,1,Semiurban,1.0
LP002110,Male,Yes,1,1,,5250,688.0,160.0,360.0,1,Rural,1.0
LP002112,Male,Yes,2,1,Yes,2500,4600.0,176.0,360.0,1,Rural,1.0
LP002113,Female,No,3+,0,No,1830,0.0,0.0,360.0,0,Urban,0.0
LP002114,Female,No,0,1,No,4160,0.0,71.0,360.0,1,Semiurban,1.0
LP002115,Male,Yes,3+,0,No,2647,1587.0,173.0,360.0,1,Rural,0.0
LP002116,Female,No,0,1,No,2378,0.0,46.0,360.0,1,Rural,0.0
LP002119,Male,Yes,1,0,No,4554,1229.0,158.0,360.0,1,Urban,1.0
LP002126,Male,Yes,3+,0,No,3173,0.0,74.0,360.0,1,Semiurban,1.0
LP002128,Male,Yes,2,1,,2583,2330.0,125.0,360.0,1,Rural,1.0
LP002129,Male,Yes,0,1,No,2499,2458.0,160.0,360.0,1,Semiurban,1.0
LP002130,Male,Yes,,0,No,3523,3230.0,152.0,360.0,0,Rural,0.0
LP002131,Male,Yes,2,0,No,3083,2168.0,126.0,360.0,1,Urban,1.0
LP002137,Male,Yes,0,1,No,6333,4583.0,259.0,360.0,0,Semiurban,1.0
LP002138,Male,Yes,0,1,No,2625,6250.0,187.0,360.0,1,Rural,1.0
LP002139,Male,Yes,0,1,No,9083,0.0,228.0,360.0,1,Semiurban,1.0
LP002140,Male,No,0,1,No,8750,4167.0,308.0,360.0,1,Rural,0.0
LP002141,Male,Yes,3+,1,No,2666,2083.0,95.0,360.0,1,Rural,1.0
LP002142,Female,Yes,0,1,Yes,5500,0.0,105.0,360.0,0,Rural,0.0
LP002143,Female,Yes,0,1,No,2423,505.0,130.0,360.0,1,Semiurban,1.0
LP002144,Female,No,,1,No,3813,0.0,116.0,180.0,1,Urban,1.0
LP002149,Male,Yes,2,1,No,8333,3167.0,165.0,360.0,1,Rural,1.0
LP002151,Male,Yes,1,1,No,3875,0.0,67.0,360.0,1,Urban,0.0
LP002158,Male,Yes,0,0,No,3000,1666.0,100.0,480.0,0,Urban,0.0
LP002160,Male,Yes,3+,1,No,5167,3167.0,200.0,360.0,1,Semiurban,1.0
LP002161,Female,No,1,1,No,4723,0.0,81.0,360.0,1,Semiurban,0.0
LP002170,Male,Yes,2,1,No,5000,3667.0,236.0,360.0,1,Semiurban,1.0
LP002175,Male,Yes,0,1,No,4750,2333.0,130.0,360.0,1,Urban,1.0
LP002178,Male,Yes,0,1,No,3013,3033.0,95.0,300.0,0,Urban,1.0
LP002180,Male,No,0,1,Yes,6822,0.0,141.0,360.0,1,Rural,1.0
LP002181,Male,No,0,0,No,6216,0.0,133.0,360.0,1,Rural,0.0
LP002187,Male,No,0,1,No,2500,0.0,96.0,480.0,1,Semiurban,0.0
LP002188,Male,No,0,1,No,5124,0.0,124.0,,0,Rural,0.0
LP002190,Male,Yes,1,1,No,6325,0.0,175.0,360.0,1,Semiurban,1.0
LP002191,Male,Yes,0,1,No,19730,5266.0,570.0,360.0,1,Rural,0.0
LP002194,Female,No,0,1,Yes,15759,0.0,55.0,360.0,1,Semiurban,1.0
LP002197,Male,Yes,2,1,No,5185,0.0,155.0,360.0,1,Semiurban,1.0
LP002201,Male,Yes,2,1,Yes,9323,7873.0,380.0,300.0,1,Rural,1.0
LP002205,Male,No,1,1,No,3062,1987.0,111.0,180.0,0,Urban,0.0
LP002209,Female,No,0,1,,2764,1459.0,110.0,360.0,1,Urban,1.0
LP002211,Male,Yes,0,1,No,4817,923.0,120.0,180.0,1,Urban,1.0
LP002219,Male,Yes,3+,1,No,8750,4996.0,130.0,360.0,1,Rural,1.0
LP002223,Male,Yes,0,1,No,4310,0.0,130.0,360.0,0,Semiurban,1.0
LP002224,Male,No,0,1,No,3069,0.0,71.0,480.0,1,Urban,0.0
LP002225,Male,Yes,2,1,No,5391,0.0,130.0,360.0,1,Urban,1.0
LP002226,Male,Yes,0,1,,3333,2500.0,128.0,360.0,1,Semiurban,1.0
LP002229,Male,No,0,1,No,5941,4232.0,296.0,360.0,1,Semiurban,1.0
LP002231,Female,No,0,1,No,6000,0.0,156.0,360.0,1,Urban,1.0
LP002234,Male,No,0,1,Yes,7167,0.0,128.0,360.0,1,Urban,1.0
LP002236,Male,Yes,2,1,No,4566,0.0,100.0,360.0,1,Urban,0.0
LP002237,Male,No,1,1,,3667,0.0,113.0,180.0,1,Urban,1.0
LP002239,Male,No,0,0,No,2346,1600.0,132.0,360.0,1,Semiurban,1.0
LP002243,Male,Yes,0,0,No,3010,3136.0,0.0,360.0,0,Urban,0.0
LP002244,Male,Yes,0,1,No,2333,2417.0,136.0,360.0,1,Urban,1.0
LP002250,Male,Yes,0,1,No,5488,0.0,125.0,360.0,1,Rural,1.0
LP002255,Male,No,3+,1,No,9167,0.0,185.0,360.0,1,Rural,1.0
LP002262,Male,Yes,3+,1,No,9504,0.0,275.0,360.0,1,Rural,1.0
LP002263,Male,Yes,0,1,No,2583,2115.0,120.0,360.0,0,Urban,1.0
LP002265,Male,Yes,2,0,No,1993,1625.0,113.0,180.0,1,Semiurban,1.0
LP002266,Male,Yes,2,1,No,3100,1400.0,113.0,360.0,1,Urban,1.0
LP002272,Male,Yes,2,1,No,3276,484.0,135.0,360.0,0,Semiurban,1.0
LP002277,Female,No,0,1,No,3180,0.0,71.0,360.0,0,Urban,0.0
LP002281,Male,Yes,0,1,No,3033,1459.0,95.0,360.0,1,Urban,1.0
LP002284,Male,No,0,0,No,3902,1666.0,109.0,360.0,1,Rural,1.0
LP002287,Female,No,0,1,No,1500,1800.0,103.0,360.0,0,Semiurban,0.0
LP002288,Male,Yes,2,0,No,2889,0.0,45.0,180.0,0,Urban,0.0
LP002296,Male,No,0,0,No,2755,0.0,65.0,300.0,1,Rural,0.0
LP002297,Male,No,0,1,No,2500,20000.0,103.0,360.0,1,Semiurban,1.0
LP002300,Female,No,0,0,No,1963,0.0,53.0,360.0,1,Semiurban,1.0
LP002301,Female,No,0,1,Yes,7441,0.0,194.0,360.0,1,Rural,0.0
LP002305,Female,No,0,1,No,4547,0.0,115.0,360.0,1,Semiurban,1.0
LP002308,Male,Yes,0,0,No,2167,2400.0,115.0,360.0,1,Urban,1.0
LP002314,Female,No,0,0,No,2213,0.0,66.0,360.0,1,Rural,1.0
LP002315,Male,Yes,1,1,No,8300,0.0,152.0,300.0,0,Semiurban,0.0
LP002317,Male,Yes,3+,1,No,81000,0.0,360.0,360.0,0,Rural,0.0
LP002318,Female,No,1,0,Yes,3867,0.0,62.0,360.0,1,Semiurban,0.0
LP002319,Male,Yes,0,1,,6256,0.0,160.0,360.0,0,Urban,1.0
LP002328,Male,Yes,0,0,No,6096,0.0,218.0,360.0,0,Rural,0.0
LP002332,Male,Yes,0,0,No,2253,2033.0,110.0,360.0,1,Rural,1.0
LP002335,Female,Yes,0,0,No,2149,3237.0,178.0,360.0,0,Semiurban,0.0
LP002337,Female,No,0,1,No,2995,0.0,60.0,360.0,1,Urban,1.0
LP002341,Female,No,1,1,No,2600,0.0,160.0,360.0,1,Urban,0.0
LP002342,Male,Yes,2,1,Yes,1600,20000.0,239.0,360.0,1,Urban,0.0
LP002345,Male,Yes,0,1,No,1025,2773.0,112.0,360.0,1,Rural,1.0
LP002347,Male,Yes,0,1,No,3246,1417.0,138.0,360.0,1,Semiurban,1.0
LP002348,Male,Yes,0,1,No,5829,0.0,138.0,360.0,1,Rural,1.0
LP002357,Female,No,0,0,No,2720,0.0,80.0,,0,Urban,0.0
LP002361,Male,Yes,0,1,No,1820,1719.0,100.0,360.0,1,Urban,1.0
LP002362,Male,Yes,1,1,No,7250,1667.0,110.0,,0,Urban,0.0
LP002364,Male,Yes,0,1,No,14880,0.0,96.0,360.0,1,Semiurban,1.0
LP002366,Male,Yes,0,1,No,2666,4300.0,121.0,360.0,1,Rural,1.0
LP002367,Female,No,1,0,No,4606,0.0,81.0,360.0,1,Rural,0.0
LP002368,Male,Yes,2,1,No,5935,0.0,133.0,360.0,1,Semiurban,1.0
LP002369,Male,Yes,0,1,No,2920,16.12000084,87.0,360.0,1,Rural,1.0
LP002370,Male,No,0,0,No,2717,0.0,60.0,180.0,1,Urban,1.0
LP002377,Female,No,1,1,Yes,8624,0.0,150.0,360.0,1,Semiurban,1.0
LP002379,Male,No,0,1,No,6500,0.0,105.0,360.0,0,Rural,0.0
LP002386,Male,No,0,1,,12876,0.0,405.0,360.0,1,Semiurban,1.0
LP002387,Male,Yes,0,1,No,2425,2340.0,143.0,360.0,1,Semiurban,1.0
LP002390,Male,No,0,1,No,3750,0.0,100.0,360.0,1,Urban,1.0
LP002393,Female,,,1,No,10047,0.0,0.0,240.0,1,Semiurban,1.0
LP002398,Male,No,0,1,No,1926,1851.0,50.0,360.0,1,Semiurban,1.0
LP002401,Male,Yes,0,1,No,2213,1125.0,0.0,360.0,1,Urban,1.0
LP002403,Male,No,0,1,Yes,10416,0.0,187.0,360.0,0,Urban,0.0
LP002407,Female,Yes,0,0,Yes,7142,0.0,138.0,360.0,1,Rural,1.0
LP002408,Male,No,0,1,No,3660,5064.0,187.0,360.0,1,Semiurban,1.0
LP002409,Male,Yes,0,1,No,7901,1833.0,180.0,360.0,1,Rural,1.0
LP002418,Male,No,3+,0,No,4707,1993.0,148.0,360.0,1,Semiurban,1.0
LP002422,Male,No,1,1,No,37719,0.0,152.0,360.0,1,Semiurban,1.0
LP002424,Male,Yes,0,1,No,7333,8333.0,175.0,300.0,0,Rural,1.0
LP002429,Male,Yes,1,1,Yes,3466,1210.0,130.0,360.0,1,Rural,1.0
LP002434,Male,Yes,2,0,No,4652,0.0,110.0,360.0,1,Rural,1.0
LP002435,Male,Yes,0,1,,3539,1376.0,55.0,360.0,1,Rural,0.0
LP002443,Male,Yes,2,1,No,3340,1710.0,150.0,360.0,0,Rural,0.0
LP002444,Male,No,1,0,Yes,2769,1542.0,190.0,360.0,0,Semiurban,0.0
LP002446,Male,Yes,2,0,No,2309,1255.0,125.0,360.0,0,Rural,0.0
LP002447,Male,Yes,2,0,No,1958,1456.0,60.0,300.0,0,Urban,1.0
LP002448,Male,Yes,0,1,No,3948,1733.0,149.0,360.0,0,Rural,0.0
LP002449,Male,Yes,0,1,No,2483,2466.0,90.0,180.0,0,Rural,1.0
LP002453,Male,No,0,1,Yes,7085,0.0,84.0,360.0,1,Semiurban,1.0
LP002455,Male,Yes,2,1,No,3859,0.0,96.0,360.0,1,Semiurban,1.0
LP002459,Male,Yes,0,1,No,4301,0.0,118.0,360.0,1,Urban,1.0
LP002467,Male,Yes,0,1,No,3708,2569.0,173.0,360.0,1,Urban,0.0
LP002472,Male,No,2,1,No,4354,0.0,136.0,360.0,1,Rural,1.0
LP002473,Male,Yes,0,1,No,8334,0.0,160.0,360.0,1,Semiurban,0.0
LP002478,,Yes,0,1,Yes,2083,4083.0,160.0,360.0,0,Semiurban,1.0
LP002484,Male,Yes,3+,1,No,7740,0.0,128.0,180.0,1,Urban,1.0
LP002487,Male,Yes,0,1,No,3015,2188.0,153.0,360.0,1,Rural,1.0
LP002489,Female,No,1,0,,5191,0.0,132.0,360.0,1,Semiurban,1.0
LP002493,Male,No,0,1,No,4166,0.0,98.0,360.0,0,Semiurban,0.0
LP002494,Male,No,0,1,No,6000,0.0,140.0,360.0,1,Rural,1.0
LP002500,Male,Yes,3+,0,No,2947,1664.0,70.0,180.0,0,Urban,0.0
LP002501,,Yes,0,1,No,16692,0.0,110.0,360.0,1,Semiurban,1.0
LP002502,Female,Yes,2,0,,210,2917.0,98.0,360.0,1,Semiurban,1.0
LP002505,Male,Yes,0,1,No,4333,2451.0,110.0,360.0,1,Urban,0.0
LP002515,Male,Yes,1,1,Yes,3450,2079.0,162.0,360.0,1,Semiurban,1.0
LP002517,Male,Yes,1,0,No,2653,1500.0,113.0,180.0,0,Rural,0.0
LP002519,Male,Yes,3+,1,No,4691,0.0,100.0,360.0,1,Semiurban,1.0
LP002522,Female,No,0,1,Yes,2500,0.0,93.0,360.0,0,Urban,1.0
LP002524,Male,No,2,1,No,5532,4648.0,162.0,360.0,1,Rural,1.0
LP002527,Male,Yes,2,1,Yes,16525,1014.0,150.0,360.0,1,Rural,1.0
LP002529,Male,Yes,2,1,No,6700,1750.0,230.0,300.0,1,Semiurban,1.0
LP002530,,Yes,2,1,No,2873,1872.0,132.0,360.0,0,Semiurban,0.0
LP002531,Male,Yes,1,1,Yes,16667,2250.0,86.0,360.0,1,Semiurban,1.0
LP002533,Male,Yes,2,1,No,2947,1603.0,0.0,360.0,1,Urban,0.0
LP002534,Female,No,0,0,No,4350,0.0,154.0,360.0,1,Rural,1.0
LP002536,Male,Yes,3+,0,No,3095,0.0,113.0,360.0,1,Rural,1.0
LP002537,Male,Yes,0,1,No,2083,3150.0,128.0,360.0,1,Semiurban,1.0
LP002541,Male,Yes,0,1,No,10833,0.0,234.0,360.0,1,Semiurban,1.0
LP002543,Male,Yes,2,1,No,8333,0.0,246.0,360.0,1,Semiurban,1.0
LP002544,Male,Yes,1,0,No,1958,2436.0,131.0,360.0,1,Rural,1.0
LP002545,Male,No,2,1,No,3547,0.0,80.0,360.0,0,Rural,0.0
LP002547,Male,Yes,1,1,No,18333,0.0,500.0,360.0,1,Urban,0.0
LP002555,Male,Yes,2,1,Yes,4583,2083.0,160.0,360.0,1,Semiurban,1.0
LP002556,Male,No,0,1,No,2435,0.0,75.0,360.0,1,Urban,0.0
LP002560,Male,No,0,0,No,2699,2785.0,96.0,360.0,0,Semiurban,1.0
LP002562,Male,Yes,1,0,No,5333,1131.0,186.0,360.0,0,Urban,1.0
LP002571,Male,No,0,0,No,3691,0.0,110.0,360.0,1,Rural,1.0
LP002582,Female,No,0,0,Yes,17263,0.0,225.0,360.0,1,Semiurban,1.0
LP002585,Male,Yes,0,1,No,3597,2157.0,119.0,360.0,0,Rural,0.0
LP002586,Female,Yes,1,1,No,3326,913.0,105.0,84.0,1,Semiurban,1.0
LP002587,Male,Yes,0,0,No,2600,1700.0,107.0,360.0,1,Rural,1.0
LP002588,Male,Yes,0,1,No,4625,2857.0,111.0,12.0,0,Urban,1.0
LP002600,Male,Yes,1,1,Yes,2895,0.0,95.0,360.0,1,Semiurban,1.0
LP002602,Male,No,0,1,No,6283,4416.0,209.0,360.0,0,Rural,0.0
LP002603,Female,No,0,1,No,645,3683.0,113.0,480.0,1,Rural,1.0
LP002606,Female,No,0,1,No,3159,0.0,100.0,360.0,1,Semiurban,1.0
LP002615,Male,Yes,2,1,No,4865,5624.0,208.0,360.0,1,Semiurban,1.0
LP002618,Male,Yes,1,0,No,4050,5302.0,138.0,360.0,0,Rural,0.0
LP002619,Male,Yes,0,0,No,3814,1483.0,124.0,300.0,1,Semiurban,1.0
LP002622,Male,Yes,2,1,No,3510,4416.0,243.0,360.0,1,Rural,1.0
LP002624,Male,Yes,0,1,No,20833,6667.0,480.0,360.0,0,Urban,1.0
LP002625,,No,0,1,No,3583,0.0,96.0,360.0,1,Urban,0.0
LP002626,Male,Yes,0,1,Yes,2479,3013.0,188.0,360.0,1,Urban,1.0
LP002634,Female,No,1,1,No,13262,0.0,40.0,360.0,1,Urban,1.0
LP002637,Male,No,0,0,No,3598,1287.0,100.0,360.0,1,Rural,0.0
LP002640,Male,Yes,1,1,No,6065,2004.0,250.0,360.0,1,Semiurban,1.0
LP002643,Male,Yes,2,1,No,3283,2035.0,148.0,360.0,1,Urban,1.0
LP002648,Male,Yes,0,1,No,2130,6666.0,70.0,180.0,1,Semiurban,0.0
LP002652,Male,No,0,1,No,5815,3666.0,311.0,360.0,1,Rural,0.0
LP002659,Male,Yes,3+,1,No,3466,3428.0,150.0,360.0,1,Rural,1.0
LP002670,Female,Yes,2,1,No,2031,1632.0,113.0,480.0,1,Semiurban,1.0
LP002682,Male,Yes,,0,No,3074,1800.0,123.0,360.0,0,Semiurban,0.0
LP002683,Male,No,0,1,No,4683,1915.0,185.0,360.0,1,Semiurban,0.0
LP002684,Female,No,0,0,No,3400,0.0,95.0,360.0,1,Rural,0.0
LP002689,Male,Yes,2,0,No,2192,1742.0,45.0,360.0,1,Semiurban,1.0
LP002690,Male,No,0,1,No,2500,0.0,55.0,360.0,1,Semiurban,1.0
LP002692,Male,Yes,3+,1,Yes,5677,1424.0,100.0,360.0,1,Rural,1.0
LP002693,Male,Yes,2,1,Yes,7948,7166.0,480.0,360.0,1,Rural,1.0
LP002697,Male,No,0,1,No,4680,2087.0,0.0,360.0,1,Semiurban,0.0
LP002699,Male,Yes,2,1,Yes,17500,0.0,400.0,360.0,1,Rural,1.0
LP002705,Male,Yes,0,1,No,3775,0.0,110.0,360.0,1,Semiurban,1.0
LP002706,Male,Yes,1,0,No,5285,1430.0,161.0,360.0,0,Semiurban,1.0
LP002714,Male,No,1,0,No,2679,1302.0,94.0,360.0,1,Semiurban,1.0
LP002716,Male,No,0,0,No,6783,0.0,130.0,360.0,1,Semiurban,1.0
LP002717,Male,Yes,0,1,No,1025,5500.0,216.0,360.0,0,Rural,1.0
LP002720,Male,Yes,3+,1,No,4281,0.0,100.0,360.0,1,Urban,1.0
LP002723,Male,No,2,1,No,3588,0.0,110.0,360.0,0,Rural,0.0
LP002729,Male,No,1,1,No,11250,0.0,196.0,360.0,0,Semiurban,0.0
LP002731,Female,No,0,0,Yes,18165,0.0,125.0,360.0,1,Urban,1.0
LP002732,Male,No,0,0,,2550,2042.0,126.0,360.0,1,Rural,1.0
LP002734,Male,Yes,0,1,No,6133,3906.0,324.0,360.0,1,Urban,1.0
LP002738,Male,No,2,1,No,3617,0.0,107.0,360.0,1,Semiurban,1.0
LP002739,Male,Yes,0,0,No,2917,536.0,66.0,360.0,1,Rural,0.0
LP002740,Male,Yes,3+,1,No,6417,0.0,157.0,180.0,1,Rural,1.0
LP002741,Female,Yes,1,1,No,4608,2845.0,140.0,180.0,1,Semiurban,1.0
LP002743,Female,No,0,1,No,2138,0.0,99.0,360.0,0,Semiurban,0.0
LP002753,Female,No,1,1,,3652,0.0,95.0,360.0,1,Semiurban,1.0
LP002755,Male,Yes,1,0,No,2239,2524.0,128.0,360.0,1,Urban,1.0
LP002757,Female,Yes,0,0,No,3017,663.0,102.0,360.0,0,Semiurban,1.0
LP002767,Male,Yes,0,1,No,2768,1950.0,155.0,360.0,1,Rural,1.0
LP002768,Male,No,0,0,No,3358,0.0,80.0,36.0,1,Semiurban,0.0
LP002772,Male,No,0,1,No,2526,1783.0,145.0,360.0,1,Rural,1.0
LP002776,Female,No,0,1,No,5000,0.0,103.0,360.0,0,Semiurban,0.0
LP002777,Male,Yes,0,1,No,2785,2016.0,110.0,360.0,1,Rural,1.0
LP002778,Male,Yes,2,1,Yes,6633,0.0,0.0,360.0,0,Rural,0.0
LP002784,Male,Yes,1,0,No,2492,2375.0,0.0,360.0,1,Rural,1.0
LP002785,Male,Yes,1,1,No,3333,3250.0,158.0,360.0,1,Urban,1.0
LP002788,Male,Yes,0,0,No,2454,2333.0,181.0,360.0,0,Urban,0.0
LP002789,Male,Yes,0,1,No,3593,4266.0,132.0,180.0,0,Rural,0.0
LP002792,Male,Yes,1,1,No,5468,1032.0,26.0,360.0,1,Semiurban,1.0
LP002794,Female,No,0,1,No,2667,1625.0,84.0,360.0,0,Urban,1.0
LP002795,Male,Yes,3+,1,Yes,10139,0.0,260.0,360.0,1,Semiurban,1.0
LP002798,Male,Yes,0,1,No,3887,2669.0,162.0,360.0,1,Semiurban,1.0
LP002804,Female,Yes,0,1,No,4180,2306.0,182.0,360.0,1,Semiurban,1.0
LP002807,Male,Yes,2,0,No,3675,242.0,108.0,360.0,1,Semiurban,1.0
LP002813,Female,Yes,1,1,Yes,19484,0.0,600.0,360.0,1,Semiurban,1.0
LP002820,Male,Yes,0,1,No,5923,2054.0,211.0,360.0,1,Rural,1.0
LP002821,Male,No,0,0,Yes,5800,0.0,132.0,360.0,1,Semiurban,1.0
LP002832,Male,Yes,2,1,No,8799,0.0,258.0,360.0,0,Urban,0.0
LP002833,Male,Yes,0,0,No,4467,0.0,120.0,360.0,0,Rural,1.0
LP002836,Male,No,0,1,No,3333,0.0,70.0,360.0,1,Urban,1.0
LP002837,Male,Yes,3+,1,No,3400,2500.0,123.0,360.0,0,Rural,0.0
LP002840,Female,No,0,1,No,2378,0.0,9.0,360.0,1,Urban,0.0
LP002841,Male,Yes,0,1,No,3166,2064.0,104.0,360.0,0,Urban,0.0
LP002842,Male,Yes,1,1,No,3417,1750.0,186.0,360.0,1,Urban,1.0
LP002847,Male,Yes,,1,No,5116,1451.0,165.0,360.0,0,Urban,0.0
LP002855,Male,Yes,2,1,No,16666,0.0,275.0,360.0,1,Urban,1.0
LP002862,Male,Yes,2,0,No,6125,1625.0,187.0,480.0,1,Semiurban,0.0
LP002863,Male,Yes,3+,1,No,6406,0.0,150.0,360.0,1,Semiurban,0.0
LP002868,Male,Yes,2,1,No,3159,461.0,108.0,84.0,1,Urban,1.0
LP002872,,Yes,0,1,No,3087,2210.0,136.0,360.0,0,Semiurban,0.0
LP002874,Male,No,0,1,No,3229,2739.0,110.0,360.0,1,Urban,1.0
LP002877,Male,Yes,1,1,No,1782,2232.0,107.0,360.0,1,Rural,1.0
LP002888,Male,No,0,1,,3182,2917.0,161.0,360.0,1,Urban,1.0
LP002892,Male,Yes,2,1,No,6540,0.0,205.0,360.0,1,Semiurban,1.0
LP002893,Male,No,0,1,No,1836,33837.0,90.0,360.0,1,Urban,0.0
LP002894,Female,Yes,0,1,No,3166,0.0,36.0,360.0,1,Semiurban,1.0
LP002898,Male,Yes,1,1,No,1880,0.0,61.0,360.0,0,Rural,0.0
LP002911,Male,Yes,1,1,No,2787,1917.0,146.0,360.0,0,Rural,0.0
LP002912,Male,Yes,1,1,No,4283,3000.0,172.0,84.0,1,Rural,0.0
LP002916,Male,Yes,0,1,No,2297,1522.0,104.0,360.0,1,Urban,1.0
LP002917,Female,No,0,0,No,2165,0.0,70.0,360.0,1,Semiurban,1.0
LP002925,,No,0,1,No,4750,0.0,94.0,360.0,1,Semiurban,1.0
LP002926,Male,Yes,2,1,Yes,2726,0.0,106.0,360.0,0,Semiurban,0.0
LP002928,Male,Yes,0,1,No,3000,3416.0,56.0,180.0,1,Semiurban,1.0
LP002931,Male,Yes,2,1,Yes,6000,0.0,205.0,240.0,1,Semiurban,0.0
LP002933,,No,3+,1,Yes,9357,0.0,292.0,360.0,1,Semiurban,1.0
LP002936,Male,Yes,0,1,No,3859,3300.0,142.0,180.0,1,Rural,1.0
LP002938,Male,Yes,0,1,Yes,16120,0.0,260.0,360.0,1,Urban,1.0
LP002940,Male,No,0,0,No,3833,0.0,110.0,360.0,1,Rural,1.0
LP002941,Male,Yes,2,0,Yes,6383,1000.0,187.0,360.0,1,Rural,0.0
LP002943,Male,No,,1,No,2987,0.0,88.0,360.0,0,Semiurban,0.0
LP002945,Male,Yes,0,1,Yes,9963,0.0,180.0,360.0,1,Rural,1.0
LP002948,Male,Yes,2,1,No,5780,0.0,192.0,360.0,1,Urban,1.0
LP002949,Female,No,3+,1,,416,41667.0,350.0,180.0,0,Urban,0.0
LP002950,Male,Yes,0,0,,2894,2792.0,155.0,360.0,1,Rural,1.0
LP002953,Male,Yes,3+,1,No,5703,0.0,128.0,360.0,1,Urban,1.0
LP002958,Male,No,0,1,No,3676,4301.0,172.0,360.0,1,Rural,1.0
LP002959,Female,Yes,1,1,No,12000,0.0,496.0,360.0,1,Semiurban,1.0
LP002960,Male,Yes,0,0,No,2400,3800.0,0.0,180.0,1,Urban,0.0
LP002961,Male,Yes,1,1,No,3400,2500.0,173.0,360.0,1,Semiurban,1.0
LP002964,Male,Yes,2,0,No,3987,1411.0,157.0,360.0,1,Rural,1.0
LP002974,Male,Yes,0,1,No,3232,1950.0,108.0,360.0,1,Rural,1.0
LP002978,Female,No,0,1,No,2900,0.0,71.0,360.0,1,Rural,1.0
LP002979,Male,Yes,3+,1,No,4106,0.0,40.0,180.0,1,Rural,1.0
LP002983,Male,Yes,1,1,No,8072,240.0,253.0,360.0,1,Urban,1.0
LP002984,Male,Yes,2,1,No,7583,0.0,187.0,360.0,1,Urban,1.0
LP002990,Female,No,0,1,Yes,4583,0.0,133.0,360.0,0,Semiurban,0.0
1 Loan_ID Gender Married Dependents Education Self_Employed ApplicantIncome CoapplicantIncome LoanAmount Loan_Amount_Term Credit_History Property_Area Loan_Status
2 LP001002 Male No 0 1 No 5849 0.0 360.0 1.0 0 Y 0.0
3 LP001003 Male Yes 1 1 No 4583 1508.0 128.0 360.0 1 Rural 0.0
4 LP001005 Male Yes 0 1 Yes 3000 0.0 66.0 360.0 1 Urban 1.0
5 LP001006 Male Yes 0 0 No 2583 2358.0 120.0 360.0 1 Urban 1.0
6 LP001008 Male No 0 1 No 6000 0.0 141.0 360.0 1 Urban 1.0
7 LP001011 Male Yes 2 1 Yes 5417 4196.0 267.0 360.0 1 Urban 1.0
8 LP001013 Male Yes 0 0 No 2333 1516.0 95.0 360.0 1 Urban 1.0
9 LP001014 Male Yes 3+ 1 No 3036 2504.0 158.0 360.0 0 Semiurban 0.0
10 LP001018 Male Yes 2 1 No 4006 1526.0 168.0 360.0 1 Urban 1.0
11 LP001020 Male Yes 1 1 No 12841 10968.0 349.0 360.0 1 Semiurban 0.0
12 LP001024 Male Yes 2 1 No 3200 700.0 70.0 360.0 1 Urban 1.0
13 LP001027 Male Yes 2 1 2500 1840.0 109.0 360.0 1 Urban 1.0
14 LP001028 Male Yes 2 1 No 3073 8106.0 200.0 360.0 1 Urban 1.0
15 LP001029 Male No 0 1 No 1853 2840.0 114.0 360.0 1 Rural 0.0
16 LP001030 Male Yes 2 1 No 1299 1086.0 17.0 120.0 1 Urban 1.0
17 LP001032 Male No 0 1 No 4950 0.0 125.0 360.0 1 Urban 1.0
18 LP001034 Male No 1 0 No 3596 0.0 100.0 240.0 0 Urban 1.0
19 LP001036 Female No 0 1 No 3510 0.0 76.0 360.0 0 Urban 0.0
20 LP001038 Male Yes 0 0 No 4887 0.0 133.0 360.0 1 Rural 0.0
21 LP001041 Male Yes 0 1 2600 3500.0 115.0 1 Urban 1.0
22 LP001043 Male Yes 0 0 No 7660 0.0 104.0 360.0 0 Urban 0.0
23 LP001046 Male Yes 1 1 No 5955 5625.0 315.0 360.0 1 Urban 1.0
24 LP001047 Male Yes 0 0 No 2600 1911.0 116.0 360.0 0 Semiurban 0.0
25 LP001050 Yes 2 0 No 3365 1917.0 112.0 360.0 0 Rural 0.0
26 LP001052 Male Yes 1 1 3717 2925.0 151.0 360.0 0 Semiurban 0.0
27 LP001066 Male Yes 0 1 Yes 9560 0.0 191.0 360.0 1 Semiurban 1.0
28 LP001068 Male Yes 0 1 No 2799 2253.0 122.0 360.0 1 Semiurban 1.0
29 LP001073 Male Yes 2 0 No 4226 1040.0 110.0 360.0 1 Urban 1.0
30 LP001086 Male No 0 0 No 1442 0.0 35.0 360.0 1 Urban 0.0
31 LP001087 Female No 2 1 3750 2083.0 120.0 360.0 1 Semiurban 1.0
32 LP001091 Male Yes 1 1 4166 3369.0 201.0 360.0 0 Urban 0.0
33 LP001095 Male No 0 1 No 3167 0.0 74.0 360.0 1 Urban 0.0
34 LP001097 Male No 1 1 Yes 4692 0.0 106.0 360.0 1 Rural 0.0
35 LP001098 Male Yes 0 1 No 3500 1667.0 114.0 360.0 1 Semiurban 1.0
36 LP001100 Male No 3+ 1 No 12500 3000.0 320.0 360.0 1 Rural 0.0
37 LP001106 Male Yes 0 1 No 2275 2067.0 0.0 360.0 1 Urban 1.0
38 LP001109 Male Yes 0 1 No 1828 1330.0 100.0 0 Urban 0.0
39 LP001112 Female Yes 0 1 No 3667 1459.0 144.0 360.0 1 Semiurban 1.0
40 LP001114 Male No 0 1 No 4166 7210.0 184.0 360.0 1 Urban 1.0
41 LP001116 Male No 0 0 No 3748 1668.0 110.0 360.0 1 Semiurban 1.0
42 LP001119 Male No 0 1 No 3600 0.0 80.0 360.0 1 Urban 0.0
43 LP001120 Male No 0 1 No 1800 1213.0 47.0 360.0 1 Urban 1.0
44 LP001123 Male Yes 0 1 No 2400 0.0 75.0 360.0 0 Urban 1.0
45 LP001131 Male Yes 0 1 No 3941 2336.0 134.0 360.0 1 Semiurban 1.0
46 LP001136 Male Yes 0 0 Yes 4695 0.0 96.0 1 Urban 1.0
47 LP001137 Female No 0 1 No 3410 0.0 88.0 1 Urban 1.0
48 LP001138 Male Yes 1 1 No 5649 0.0 44.0 360.0 1 Urban 1.0
49 LP001144 Male Yes 0 1 No 5821 0.0 144.0 360.0 1 Urban 1.0
50 LP001146 Female Yes 0 1 No 2645 3440.0 120.0 360.0 0 Urban 0.0
51 LP001151 Female No 0 1 No 4000 2275.0 144.0 360.0 1 Semiurban 1.0
52 LP001155 Female Yes 0 0 No 1928 1644.0 100.0 360.0 1 Semiurban 1.0
53 LP001157 Female No 0 1 No 3086 0.0 120.0 360.0 1 Semiurban 1.0
54 LP001164 Female No 0 1 No 4230 0.0 112.0 360.0 1 Semiurban 0.0
55 LP001179 Male Yes 2 1 No 4616 0.0 134.0 360.0 1 Urban 0.0
56 LP001186 Female Yes 1 1 Yes 11500 0.0 286.0 360.0 0 Urban 0.0
57 LP001194 Male Yes 2 1 No 2708 1167.0 97.0 360.0 1 Semiurban 1.0
58 LP001195 Male Yes 0 1 No 2132 1591.0 96.0 360.0 1 Semiurban 1.0
59 LP001197 Male Yes 0 1 No 3366 2200.0 135.0 360.0 1 Rural 0.0
60 LP001198 Male Yes 1 1 No 8080 2250.0 180.0 360.0 1 Urban 1.0
61 LP001199 Male Yes 2 0 No 3357 2859.0 144.0 360.0 1 Urban 1.0
62 LP001205 Male Yes 0 1 No 2500 3796.0 120.0 360.0 1 Urban 1.0
63 LP001206 Male Yes 3+ 1 No 3029 0.0 99.0 360.0 1 Urban 1.0
64 LP001207 Male Yes 0 0 Yes 2609 3449.0 165.0 180.0 0 Rural 0.0
65 LP001213 Male Yes 1 1 No 4945 0.0 0.0 360.0 0 Rural 0.0
66 LP001222 Female No 0 1 No 4166 0.0 116.0 360.0 0 Semiurban 0.0
67 LP001225 Male Yes 0 1 No 5726 4595.0 258.0 360.0 1 Semiurban 0.0
68 LP001228 Male No 0 0 No 3200 2254.0 126.0 180.0 0 Urban 0.0
69 LP001233 Male Yes 1 1 No 10750 0.0 312.0 360.0 1 Urban 1.0
70 LP001238 Male Yes 3+ 0 Yes 7100 0.0 125.0 60.0 1 Urban 1.0
71 LP001241 Female No 0 1 No 4300 0.0 136.0 360.0 0 Semiurban 0.0
72 LP001243 Male Yes 0 1 No 3208 3066.0 172.0 360.0 1 Urban 1.0
73 LP001245 Male Yes 2 0 Yes 1875 1875.0 97.0 360.0 1 Semiurban 1.0
74 LP001248 Male No 0 1 No 3500 0.0 81.0 300.0 1 Semiurban 1.0
75 LP001250 Male Yes 3+ 0 No 4755 0.0 95.0 0 Semiurban 0.0
76 LP001253 Male Yes 3+ 1 Yes 5266 1774.0 187.0 360.0 1 Semiurban 1.0
77 LP001255 Male No 0 1 No 3750 0.0 113.0 480.0 1 Urban 0.0
78 LP001256 Male No 0 1 No 3750 4750.0 176.0 360.0 1 Urban 0.0
79 LP001259 Male Yes 1 1 Yes 1000 3022.0 110.0 360.0 1 Urban 0.0
80 LP001263 Male Yes 3+ 1 No 3167 4000.0 180.0 300.0 0 Semiurban 0.0
81 LP001264 Male Yes 3+ 0 Yes 3333 2166.0 130.0 360.0 0 Semiurban 1.0
82 LP001265 Female No 0 1 No 3846 0.0 111.0 360.0 1 Semiurban 1.0
83 LP001266 Male Yes 1 1 Yes 2395 0.0 0.0 360.0 1 Semiurban 1.0
84 LP001267 Female Yes 2 1 No 1378 1881.0 167.0 360.0 1 Urban 0.0
85 LP001273 Male Yes 0 1 No 6000 2250.0 265.0 360.0 0 Semiurban 0.0
86 LP001275 Male Yes 1 1 No 3988 0.0 50.0 240.0 1 Urban 1.0
87 LP001279 Male No 0 1 No 2366 2531.0 136.0 360.0 1 Semiurban 1.0
88 LP001280 Male Yes 2 0 No 3333 2000.0 99.0 360.0 0 Semiurban 1.0
89 LP001282 Male Yes 0 1 No 2500 2118.0 104.0 360.0 1 Semiurban 1.0
90 LP001289 Male No 0 1 No 8566 0.0 210.0 360.0 1 Urban 1.0
91 LP001310 Male Yes 0 1 No 5695 4167.0 175.0 360.0 1 Semiurban 1.0
92 LP001316 Male Yes 0 1 No 2958 2900.0 131.0 360.0 1 Semiurban 1.0
93 LP001318 Male Yes 2 1 No 6250 5654.0 188.0 180.0 1 Semiurban 1.0
94 LP001319 Male Yes 2 0 No 3273 1820.0 81.0 360.0 1 Urban 1.0
95 LP001322 Male No 0 1 No 4133 0.0 122.0 360.0 1 Semiurban 1.0
96 LP001325 Male No 0 0 No 3620 0.0 25.0 120.0 1 Semiurban 1.0
97 LP001326 Male No 0 1 6782 0.0 0.0 360.0 0 Urban 0.0
98 LP001327 Female Yes 0 1 No 2484 2302.0 137.0 360.0 1 Semiurban 1.0
99 LP001333 Male Yes 0 1 No 1977 997.0 50.0 360.0 1 Semiurban 1.0
100 LP001334 Male Yes 0 0 No 4188 0.0 115.0 180.0 1 Semiurban 1.0
101 LP001343 Male Yes 0 1 No 1759 3541.0 131.0 360.0 1 Semiurban 1.0
102 LP001345 Male Yes 2 0 No 4288 3263.0 133.0 180.0 1 Urban 1.0
103 LP001349 Male No 0 1 No 4843 3806.0 151.0 360.0 1 Semiurban 1.0
104 LP001350 Male Yes 1 No 13650 0.0 0.0 360.0 1 Urban 1.0
105 LP001356 Male Yes 0 1 No 4652 3583.0 0.0 360.0 1 Semiurban 1.0
106 LP001357 Male 1 No 3816 754.0 160.0 360.0 1 Urban 1.0
107 LP001367 Male Yes 1 1 No 3052 1030.0 100.0 360.0 1 Urban 1.0
108 LP001369 Male Yes 2 1 No 11417 1126.0 225.0 360.0 1 Urban 1.0
109 LP001370 Male No 0 0 7333 0.0 120.0 360.0 1 Rural 0.0
110 LP001379 Male Yes 2 1 No 3800 3600.0 216.0 360.0 0 Urban 0.0
111 LP001384 Male Yes 3+ 0 No 2071 754.0 94.0 480.0 1 Semiurban 1.0
112 LP001385 Male No 0 1 No 5316 0.0 136.0 360.0 1 Urban 1.0
113 LP001387 Female Yes 0 1 2929 2333.0 139.0 360.0 1 Semiurban 1.0
114 LP001391 Male Yes 0 0 No 3572 4114.0 152.0 0 Rural 0.0
115 LP001392 Female No 1 1 Yes 7451 0.0 0.0 360.0 1 Semiurban 1.0
116 LP001398 Male No 0 1 5050 0.0 118.0 360.0 1 Semiurban 1.0
117 LP001401 Male Yes 1 1 No 14583 0.0 185.0 180.0 1 Rural 1.0
118 LP001404 Female Yes 0 1 No 3167 2283.0 154.0 360.0 1 Semiurban 1.0
119 LP001405 Male Yes 1 1 No 2214 1398.0 85.0 360.0 0 Urban 1.0
120 LP001421 Male Yes 0 1 No 5568 2142.0 175.0 360.0 1 Rural 0.0
121 LP001422 Female No 0 1 No 10408 0.0 259.0 360.0 1 Urban 1.0
122 LP001426 Male Yes 1 No 5667 2667.0 180.0 360.0 1 Rural 1.0
123 LP001430 Female No 0 1 No 4166 0.0 44.0 360.0 1 Semiurban 1.0
124 LP001431 Female No 0 1 No 2137 8980.0 137.0 360.0 0 Semiurban 1.0
125 LP001432 Male Yes 2 1 No 2957 0.0 81.0 360.0 1 Semiurban 1.0
126 LP001439 Male Yes 0 0 No 4300 2014.0 194.0 360.0 1 Rural 1.0
127 LP001443 Female No 0 1 No 3692 0.0 93.0 360.0 0 Rural 1.0
128 LP001448 Yes 3+ 1 No 23803 0.0 370.0 360.0 1 Rural 1.0
129 LP001449 Male No 0 1 No 3865 1640.0 0.0 360.0 1 Rural 1.0
130 LP001451 Male Yes 1 1 Yes 10513 3850.0 160.0 180.0 0 Urban 0.0
131 LP001465 Male Yes 0 1 No 6080 2569.0 182.0 360.0 0 Rural 0.0
132 LP001469 Male No 0 1 Yes 20166 0.0 650.0 480.0 0 Urban 1.0
133 LP001473 Male No 0 1 No 2014 1929.0 74.0 360.0 1 Urban 1.0
134 LP001478 Male No 0 1 No 2718 0.0 70.0 360.0 1 Semiurban 1.0
135 LP001482 Male Yes 0 1 Yes 3459 0.0 25.0 120.0 1 Semiurban 1.0
136 LP001487 Male No 0 1 No 4895 0.0 102.0 360.0 1 Semiurban 1.0
137 LP001488 Male Yes 3+ 1 No 4000 7750.0 290.0 360.0 1 Semiurban 0.0
138 LP001489 Female Yes 0 1 No 4583 0.0 84.0 360.0 1 Rural 0.0
139 LP001491 Male Yes 2 1 Yes 3316 3500.0 88.0 360.0 1 Urban 1.0
140 LP001492 Male No 0 1 No 14999 0.0 242.0 360.0 0 Semiurban 0.0
141 LP001493 Male Yes 2 0 No 4200 1430.0 129.0 360.0 1 Rural 0.0
142 LP001497 Male Yes 2 1 No 5042 2083.0 185.0 360.0 1 Rural 0.0
143 LP001498 Male No 0 1 No 5417 0.0 168.0 360.0 1 Urban 1.0
144 LP001504 Male No 0 1 Yes 6950 0.0 175.0 180.0 1 Semiurban 1.0
145 LP001507 Male Yes 0 1 No 2698 2034.0 122.0 360.0 1 Semiurban 1.0
146 LP001508 Male Yes 2 1 No 11757 0.0 187.0 180.0 1 Urban 1.0
147 LP001514 Female Yes 0 1 No 2330 4486.0 100.0 360.0 1 Semiurban 1.0
148 LP001516 Female Yes 2 1 No 14866 0.0 70.0 360.0 1 Urban 1.0
149 LP001518 Male Yes 1 1 No 1538 1425.0 30.0 360.0 1 Urban 1.0
150 LP001519 Female No 0 1 No 10000 1666.0 225.0 360.0 1 Rural 0.0
151 LP001520 Male Yes 0 1 No 4860 830.0 125.0 360.0 1 Semiurban 1.0
152 LP001528 Male No 0 1 No 6277 0.0 118.0 360.0 0 Rural 0.0
153 LP001529 Male Yes 0 1 Yes 2577 3750.0 152.0 360.0 1 Rural 1.0
154 LP001531 Male No 0 1 No 9166 0.0 244.0 360.0 1 Urban 0.0
155 LP001532 Male Yes 2 0 No 2281 0.0 113.0 360.0 1 Rural 0.0
156 LP001535 Male No 0 1 No 3254 0.0 50.0 360.0 1 Urban 1.0
157 LP001536 Male Yes 3+ 1 No 39999 0.0 600.0 180.0 0 Semiurban 1.0
158 LP001541 Male Yes 1 1 No 6000 0.0 160.0 360.0 0 Rural 1.0
159 LP001543 Male Yes 1 1 No 9538 0.0 187.0 360.0 1 Urban 1.0
160 LP001546 Male No 0 1 2980 2083.0 120.0 360.0 1 Rural 1.0
161 LP001552 Male Yes 0 1 No 4583 5625.0 255.0 360.0 1 Semiurban 1.0
162 LP001560 Male Yes 0 0 No 1863 1041.0 98.0 360.0 1 Semiurban 1.0
163 LP001562 Male Yes 0 1 No 7933 0.0 275.0 360.0 1 Urban 0.0
164 LP001565 Male Yes 1 1 No 3089 1280.0 121.0 360.0 0 Semiurban 0.0
165 LP001570 Male Yes 2 1 No 4167 1447.0 158.0 360.0 1 Rural 1.0
166 LP001572 Male Yes 0 1 No 9323 0.0 75.0 180.0 1 Urban 1.0
167 LP001574 Male Yes 0 1 No 3707 3166.0 182.0 1 Rural 1.0
168 LP001577 Female Yes 0 1 No 4583 0.0 112.0 360.0 1 Rural 0.0
169 LP001578 Male Yes 0 1 No 2439 3333.0 129.0 360.0 1 Rural 1.0
170 LP001579 Male No 0 1 No 2237 0.0 63.0 480.0 0 Semiurban 0.0
171 LP001580 Male Yes 2 1 No 8000 0.0 200.0 360.0 1 Semiurban 1.0
172 LP001581 Male Yes 0 0 1820 1769.0 95.0 360.0 1 Rural 1.0
173 LP001585 Yes 3+ 1 No 51763 0.0 700.0 300.0 1 Urban 1.0
174 LP001586 Male Yes 3+ 0 No 3522 0.0 81.0 180.0 1 Rural 0.0
175 LP001594 Male Yes 0 1 No 5708 5625.0 187.0 360.0 1 Semiurban 1.0
176 LP001603 Male Yes 0 0 Yes 4344 736.0 87.0 360.0 1 Semiurban 0.0
177 LP001606 Male Yes 0 1 No 3497 1964.0 116.0 360.0 1 Rural 1.0
178 LP001608 Male Yes 2 1 No 2045 1619.0 101.0 360.0 1 Rural 1.0
179 LP001610 Male Yes 3+ 1 No 5516 11300.0 495.0 360.0 0 Semiurban 0.0
180 LP001616 Male Yes 1 1 No 3750 0.0 116.0 360.0 1 Semiurban 1.0
181 LP001630 Male No 0 0 No 2333 1451.0 102.0 480.0 0 Urban 0.0
182 LP001633 Male Yes 1 1 No 6400 7250.0 180.0 360.0 0 Urban 0.0
183 LP001634 Male No 0 1 No 1916 5063.0 67.0 360.0 0 Rural 0.0
184 LP001636 Male Yes 0 1 No 4600 0.0 73.0 180.0 1 Semiurban 1.0
185 LP001637 Male Yes 1 1 No 33846 0.0 260.0 360.0 1 Semiurban 0.0
186 LP001639 Female Yes 0 1 No 3625 0.0 108.0 360.0 1 Semiurban 1.0
187 LP001640 Male Yes 0 1 Yes 39147 4750.0 120.0 360.0 1 Semiurban 1.0
188 LP001641 Male Yes 1 1 Yes 2178 0.0 66.0 300.0 0 Rural 0.0
189 LP001643 Male Yes 0 1 No 2383 2138.0 58.0 360.0 0 Rural 1.0
190 LP001644 Yes 0 1 Yes 674 5296.0 168.0 360.0 1 Rural 1.0
191 LP001647 Male Yes 0 1 No 9328 0.0 188.0 180.0 1 Rural 1.0
192 LP001653 Male No 0 0 No 4885 0.0 48.0 360.0 1 Rural 1.0
193 LP001656 Male No 0 1 No 12000 0.0 164.0 360.0 1 Semiurban 0.0
194 LP001657 Male Yes 0 0 No 6033 0.0 160.0 360.0 1 Urban 0.0
195 LP001658 Male No 0 1 No 3858 0.0 76.0 360.0 1 Semiurban 1.0
196 LP001664 Male No 0 1 No 4191 0.0 120.0 360.0 1 Rural 1.0
197 LP001665 Male Yes 1 1 No 3125 2583.0 170.0 360.0 1 Semiurban 0.0
198 LP001666 Male No 0 1 No 8333 3750.0 187.0 360.0 1 Rural 1.0
199 LP001669 Female No 0 0 No 1907 2365.0 120.0 1 Urban 1.0
200 LP001671 Female Yes 0 1 No 3416 2816.0 113.0 360.0 0 Semiurban 1.0
201 LP001673 Male No 0 1 Yes 11000 0.0 83.0 360.0 1 Urban 0.0
202 LP001674 Male Yes 1 0 No 2600 2500.0 90.0 360.0 1 Semiurban 1.0
203 LP001677 Male No 2 1 No 4923 0.0 166.0 360.0 0 Semiurban 1.0
204 LP001682 Male Yes 3+ 0 No 3992 0.0 0.0 180.0 1 Urban 0.0
205 LP001688 Male Yes 1 0 No 3500 1083.0 135.0 360.0 1 Urban 1.0
206 LP001691 Male Yes 2 0 No 3917 0.0 124.0 360.0 1 Semiurban 1.0
207 LP001692 Female No 0 0 No 4408 0.0 120.0 360.0 1 Semiurban 1.0
208 LP001693 Female No 0 1 No 3244 0.0 80.0 360.0 1 Urban 1.0
209 LP001698 Male No 0 0 No 3975 2531.0 55.0 360.0 1 Rural 1.0
210 LP001699 Male No 0 1 No 2479 0.0 59.0 360.0 1 Urban 1.0
211 LP001702 Male No 0 1 No 3418 0.0 127.0 360.0 1 Semiurban 0.0
212 LP001708 Female No 0 1 No 10000 0.0 214.0 360.0 1 Semiurban 0.0
213 LP001711 Male Yes 3+ 1 No 3430 1250.0 128.0 360.0 0 Semiurban 0.0
214 LP001713 Male Yes 1 1 Yes 7787 0.0 240.0 360.0 1 Urban 1.0
215 LP001715 Male Yes 3+ 0 Yes 5703 0.0 130.0 360.0 1 Rural 1.0
216 LP001716 Male Yes 0 1 No 3173 3021.0 137.0 360.0 1 Urban 1.0
217 LP001720 Male Yes 3+ 0 No 3850 983.0 100.0 360.0 1 Semiurban 1.0
218 LP001722 Male Yes 0 1 No 150 1800.0 135.0 360.0 1 Rural 0.0
219 LP001726 Male Yes 0 1 No 3727 1775.0 131.0 360.0 1 Semiurban 1.0
220 LP001732 Male Yes 2 1 5000 0.0 72.0 360.0 0 Semiurban 0.0
221 LP001734 Female Yes 2 1 No 4283 2383.0 127.0 360.0 0 Semiurban 1.0
222 LP001736 Male Yes 0 1 No 2221 0.0 60.0 360.0 0 Urban 0.0
223 LP001743 Male Yes 2 1 No 4009 1717.0 116.0 360.0 1 Semiurban 1.0
224 LP001744 Male No 0 1 No 2971 2791.0 144.0 360.0 1 Semiurban 1.0
225 LP001749 Male Yes 0 1 No 7578 1010.0 175.0 1 Semiurban 1.0
226 LP001750 Male Yes 0 1 No 6250 0.0 128.0 360.0 1 Semiurban 1.0
227 LP001751 Male Yes 0 1 No 3250 0.0 170.0 360.0 1 Rural 0.0
228 LP001754 Male Yes 0 Yes 4735 0.0 138.0 360.0 1 Urban 0.0
229 LP001758 Male Yes 2 1 No 6250 1695.0 210.0 360.0 1 Semiurban 1.0
230 LP001760 Male 1 No 4758 0.0 158.0 480.0 1 Semiurban 1.0
231 LP001761 Male No 0 1 Yes 6400 0.0 200.0 360.0 1 Rural 1.0
232 LP001765 Male Yes 1 1 No 2491 2054.0 104.0 360.0 1 Semiurban 1.0
233 LP001768 Male Yes 0 1 3716 0.0 42.0 180.0 1 Rural 1.0
234 LP001770 Male No 0 0 No 3189 2598.0 120.0 1 Rural 1.0
235 LP001776 Female No 0 1 No 8333 0.0 280.0 360.0 1 Semiurban 1.0
236 LP001778 Male Yes 1 1 No 3155 1779.0 140.0 360.0 1 Semiurban 1.0
237 LP001784 Male Yes 1 1 No 5500 1260.0 170.0 360.0 1 Rural 1.0
238 LP001786 Male Yes 0 1 5746 0.0 255.0 360.0 0 Urban 0.0
239 LP001788 Female No 0 1 Yes 3463 0.0 122.0 360.0 0 Urban 1.0
240 LP001790 Female No 1 1 No 3812 0.0 112.0 360.0 1 Rural 1.0
241 LP001792 Male Yes 1 1 No 3315 0.0 96.0 360.0 1 Semiurban 1.0
242 LP001798 Male Yes 2 1 No 5819 5000.0 120.0 360.0 1 Rural 1.0
243 LP001800 Male Yes 1 0 No 2510 1983.0 140.0 180.0 1 Urban 0.0
244 LP001806 Male No 0 1 No 2965 5701.0 155.0 60.0 1 Urban 1.0
245 LP001807 Male Yes 2 1 Yes 6250 1300.0 108.0 360.0 1 Rural 1.0
246 LP001811 Male Yes 0 0 No 3406 4417.0 123.0 360.0 1 Semiurban 1.0
247 LP001813 Male No 0 1 Yes 6050 4333.0 120.0 180.0 1 Urban 0.0
248 LP001814 Male Yes 2 1 No 9703 0.0 112.0 360.0 1 Urban 1.0
249 LP001819 Male Yes 1 0 No 6608 0.0 137.0 180.0 1 Urban 1.0
250 LP001824 Male Yes 1 1 No 2882 1843.0 123.0 480.0 1 Semiurban 1.0
251 LP001825 Male Yes 0 1 No 1809 1868.0 90.0 360.0 1 Urban 1.0
252 LP001835 Male Yes 0 0 No 1668 3890.0 201.0 360.0 0 Semiurban 0.0
253 LP001836 Female No 2 1 No 3427 0.0 138.0 360.0 1 Urban 0.0
254 LP001841 Male No 0 0 Yes 2583 2167.0 104.0 360.0 1 Rural 1.0
255 LP001843 Male Yes 1 0 No 2661 7101.0 279.0 180.0 1 Semiurban 1.0
256 LP001844 Male No 0 1 Yes 16250 0.0 192.0 360.0 0 Urban 0.0
257 LP001846 Female No 3+ 1 No 3083 0.0 255.0 360.0 1 Rural 1.0
258 LP001849 Male No 0 0 No 6045 0.0 115.0 360.0 0 Rural 0.0
259 LP001854 Male Yes 3+ 1 No 5250 0.0 94.0 360.0 1 Urban 0.0
260 LP001859 Male Yes 0 1 No 14683 2100.0 304.0 360.0 1 Rural 0.0
261 LP001864 Male Yes 3+ 0 No 4931 0.0 128.0 360.0 0 Semiurban 0.0
262 LP001865 Male Yes 1 1 No 6083 4250.0 330.0 360.0 0 Urban 1.0
263 LP001868 Male No 0 1 No 2060 2209.0 134.0 360.0 1 Semiurban 1.0
264 LP001870 Female No 1 1 No 3481 0.0 155.0 36.0 1 Semiurban 0.0
265 LP001871 Female No 0 1 No 7200 0.0 120.0 360.0 1 Rural 1.0
266 LP001872 Male No 0 1 Yes 5166 0.0 128.0 360.0 1 Semiurban 1.0
267 LP001875 Male No 0 1 No 4095 3447.0 151.0 360.0 1 Rural 1.0
268 LP001877 Male Yes 2 1 No 4708 1387.0 150.0 360.0 1 Semiurban 1.0
269 LP001882 Male Yes 3+ 1 No 4333 1811.0 160.0 360.0 0 Urban 1.0
270 LP001883 Female No 0 1 3418 0.0 135.0 360.0 1 Rural 0.0
271 LP001884 Female No 1 1 No 2876 1560.0 90.0 360.0 1 Urban 1.0
272 LP001888 Female No 0 1 No 3237 0.0 30.0 360.0 1 Urban 1.0
273 LP001891 Male Yes 0 1 No 11146 0.0 136.0 360.0 1 Urban 1.0
274 LP001892 Male No 0 1 No 2833 1857.0 126.0 360.0 1 Rural 1.0
275 LP001894 Male Yes 0 1 No 2620 2223.0 150.0 360.0 1 Semiurban 1.0
276 LP001896 Male Yes 2 1 No 3900 0.0 90.0 360.0 1 Semiurban 1.0
277 LP001900 Male Yes 1 1 No 2750 1842.0 115.0 360.0 1 Semiurban 1.0
278 LP001903 Male Yes 0 1 No 3993 3274.0 207.0 360.0 1 Semiurban 1.0
279 LP001904 Male Yes 0 1 No 3103 1300.0 80.0 360.0 1 Urban 1.0
280 LP001907 Male Yes 0 1 No 14583 0.0 436.0 360.0 1 Semiurban 1.0
281 LP001908 Female Yes 0 0 No 4100 0.0 124.0 360.0 0 Rural 1.0
282 LP001910 Male No 1 0 Yes 4053 2426.0 158.0 360.0 0 Urban 0.0
283 LP001914 Male Yes 0 1 No 3927 800.0 112.0 360.0 1 Semiurban 1.0
284 LP001915 Male Yes 2 1 No 2301 985.7999878 78.0 180.0 1 Urban 1.0
285 LP001917 Female No 0 1 No 1811 1666.0 54.0 360.0 1 Urban 1.0
286 LP001922 Male Yes 0 1 No 20667 0.0 0.0 360.0 1 Rural 0.0
287 LP001924 Male No 0 1 No 3158 3053.0 89.0 360.0 1 Rural 1.0
288 LP001925 Female No 0 1 Yes 2600 1717.0 99.0 300.0 1 Semiurban 0.0
289 LP001926 Male Yes 0 1 No 3704 2000.0 120.0 360.0 1 Rural 1.0
290 LP001931 Female No 0 1 No 4124 0.0 115.0 360.0 1 Semiurban 1.0
291 LP001935 Male No 0 1 No 9508 0.0 187.0 360.0 1 Rural 1.0
292 LP001936 Male Yes 0 1 No 3075 2416.0 139.0 360.0 1 Rural 1.0
293 LP001938 Male Yes 2 1 No 4400 0.0 127.0 360.0 0 Semiurban 0.0
294 LP001940 Male Yes 2 1 No 3153 1560.0 134.0 360.0 1 Urban 1.0
295 LP001945 Female No 1 No 5417 0.0 143.0 480.0 0 Urban 0.0
296 LP001947 Male Yes 0 1 No 2383 3334.0 172.0 360.0 1 Semiurban 1.0
297 LP001949 Male Yes 3+ 1 4416 1250.0 110.0 360.0 1 Urban 1.0
298 LP001953 Male Yes 1 1 No 6875 0.0 200.0 360.0 1 Semiurban 1.0
299 LP001954 Female Yes 1 1 No 4666 0.0 135.0 360.0 1 Urban 1.0
300 LP001955 Female No 0 1 No 5000 2541.0 151.0 480.0 1 Rural 0.0
301 LP001963 Male Yes 1 1 No 2014 2925.0 113.0 360.0 1 Urban 0.0
302 LP001964 Male Yes 0 0 No 1800 2934.0 93.0 360.0 0 Urban 0.0
303 LP001972 Male Yes 0 No 2875 1750.0 105.0 360.0 1 Semiurban 1.0
304 LP001974 Female No 0 1 No 5000 0.0 132.0 360.0 1 Rural 1.0
305 LP001977 Male Yes 1 1 No 1625 1803.0 96.0 360.0 1 Urban 1.0
306 LP001978 Male No 0 1 No 4000 2500.0 140.0 360.0 1 Rural 1.0
307 LP001990 Male No 0 0 No 2000 0.0 0.0 360.0 1 Urban 0.0
308 LP001993 Female No 0 1 No 3762 1666.0 135.0 360.0 1 Rural 1.0
309 LP001994 Female No 0 1 No 2400 1863.0 104.0 360.0 0 Urban 0.0
310 LP001996 Male No 0 1 No 20233 0.0 480.0 360.0 1 Rural 0.0
311 LP001998 Male Yes 2 0 No 7667 0.0 185.0 360.0 0 Rural 1.0
312 LP002002 Female No 0 1 No 2917 0.0 84.0 360.0 1 Semiurban 1.0
313 LP002004 Male No 0 0 No 2927 2405.0 111.0 360.0 1 Semiurban 1.0
314 LP002006 Female No 0 1 No 2507 0.0 56.0 360.0 1 Rural 1.0
315 LP002008 Male Yes 2 1 Yes 5746 0.0 144.0 84.0 0 Rural 1.0
316 LP002024 Yes 0 1 No 2473 1843.0 159.0 360.0 1 Rural 0.0
317 LP002031 Male Yes 1 0 No 3399 1640.0 111.0 180.0 1 Urban 1.0
318 LP002035 Male Yes 2 1 No 3717 0.0 120.0 360.0 1 Semiurban 1.0
319 LP002036 Male Yes 0 1 No 2058 2134.0 88.0 360.0 0 Urban 1.0
320 LP002043 Female No 1 1 No 3541 0.0 112.0 360.0 0 Semiurban 1.0
321 LP002050 Male Yes 1 1 Yes 10000 0.0 155.0 360.0 1 Rural 0.0
322 LP002051 Male Yes 0 1 No 2400 2167.0 115.0 360.0 1 Semiurban 1.0
323 LP002053 Male Yes 3+ 1 No 4342 189.0 124.0 360.0 1 Semiurban 1.0
324 LP002054 Male Yes 2 0 No 3601 1590.0 0.0 360.0 1 Rural 1.0
325 LP002055 Female No 0 1 No 3166 2985.0 132.0 360.0 0 Rural 1.0
326 LP002065 Male Yes 3+ 1 No 15000 0.0 300.0 360.0 1 Rural 1.0
327 LP002067 Male Yes 1 1 Yes 8666 4983.0 376.0 360.0 0 Rural 0.0
328 LP002068 Male No 0 1 No 4917 0.0 130.0 360.0 0 Rural 1.0
329 LP002082 Male Yes 0 1 Yes 5818 2160.0 184.0 360.0 1 Semiurban 1.0
330 LP002086 Female Yes 0 1 No 4333 2451.0 110.0 360.0 1 Urban 0.0
331 LP002087 Female No 0 1 No 2500 0.0 67.0 360.0 1 Urban 1.0
332 LP002097 Male No 1 1 No 4384 1793.0 117.0 360.0 1 Urban 1.0
333 LP002098 Male No 0 1 No 2935 0.0 98.0 360.0 1 Semiurban 1.0
334 LP002100 Male No 1 No 2833 0.0 71.0 360.0 1 Urban 1.0
335 LP002101 Male Yes 0 1 63337 0.0 490.0 180.0 1 Urban 1.0
336 LP002103 Yes 1 1 Yes 9833 1833.0 182.0 180.0 1 Urban 1.0
337 LP002106 Male Yes 1 Yes 5503 4490.0 70.0 1 Semiurban 1.0
338 LP002110 Male Yes 1 1 5250 688.0 160.0 360.0 1 Rural 1.0
339 LP002112 Male Yes 2 1 Yes 2500 4600.0 176.0 360.0 1 Rural 1.0
340 LP002113 Female No 3+ 0 No 1830 0.0 0.0 360.0 0 Urban 0.0
341 LP002114 Female No 0 1 No 4160 0.0 71.0 360.0 1 Semiurban 1.0
342 LP002115 Male Yes 3+ 0 No 2647 1587.0 173.0 360.0 1 Rural 0.0
343 LP002116 Female No 0 1 No 2378 0.0 46.0 360.0 1 Rural 0.0
344 LP002119 Male Yes 1 0 No 4554 1229.0 158.0 360.0 1 Urban 1.0
345 LP002126 Male Yes 3+ 0 No 3173 0.0 74.0 360.0 1 Semiurban 1.0
346 LP002128 Male Yes 2 1 2583 2330.0 125.0 360.0 1 Rural 1.0
347 LP002129 Male Yes 0 1 No 2499 2458.0 160.0 360.0 1 Semiurban 1.0
348 LP002130 Male Yes 0 No 3523 3230.0 152.0 360.0 0 Rural 0.0
349 LP002131 Male Yes 2 0 No 3083 2168.0 126.0 360.0 1 Urban 1.0
350 LP002137 Male Yes 0 1 No 6333 4583.0 259.0 360.0 0 Semiurban 1.0
351 LP002138 Male Yes 0 1 No 2625 6250.0 187.0 360.0 1 Rural 1.0
352 LP002139 Male Yes 0 1 No 9083 0.0 228.0 360.0 1 Semiurban 1.0
353 LP002140 Male No 0 1 No 8750 4167.0 308.0 360.0 1 Rural 0.0
354 LP002141 Male Yes 3+ 1 No 2666 2083.0 95.0 360.0 1 Rural 1.0
355 LP002142 Female Yes 0 1 Yes 5500 0.0 105.0 360.0 0 Rural 0.0
356 LP002143 Female Yes 0 1 No 2423 505.0 130.0 360.0 1 Semiurban 1.0
357 LP002144 Female No 1 No 3813 0.0 116.0 180.0 1 Urban 1.0
358 LP002149 Male Yes 2 1 No 8333 3167.0 165.0 360.0 1 Rural 1.0
359 LP002151 Male Yes 1 1 No 3875 0.0 67.0 360.0 1 Urban 0.0
360 LP002158 Male Yes 0 0 No 3000 1666.0 100.0 480.0 0 Urban 0.0
361 LP002160 Male Yes 3+ 1 No 5167 3167.0 200.0 360.0 1 Semiurban 1.0
362 LP002161 Female No 1 1 No 4723 0.0 81.0 360.0 1 Semiurban 0.0
363 LP002170 Male Yes 2 1 No 5000 3667.0 236.0 360.0 1 Semiurban 1.0
364 LP002175 Male Yes 0 1 No 4750 2333.0 130.0 360.0 1 Urban 1.0
365 LP002178 Male Yes 0 1 No 3013 3033.0 95.0 300.0 0 Urban 1.0
366 LP002180 Male No 0 1 Yes 6822 0.0 141.0 360.0 1 Rural 1.0
367 LP002181 Male No 0 0 No 6216 0.0 133.0 360.0 1 Rural 0.0
368 LP002187 Male No 0 1 No 2500 0.0 96.0 480.0 1 Semiurban 0.0
369 LP002188 Male No 0 1 No 5124 0.0 124.0 0 Rural 0.0
370 LP002190 Male Yes 1 1 No 6325 0.0 175.0 360.0 1 Semiurban 1.0
371 LP002191 Male Yes 0 1 No 19730 5266.0 570.0 360.0 1 Rural 0.0
372 LP002194 Female No 0 1 Yes 15759 0.0 55.0 360.0 1 Semiurban 1.0
373 LP002197 Male Yes 2 1 No 5185 0.0 155.0 360.0 1 Semiurban 1.0
374 LP002201 Male Yes 2 1 Yes 9323 7873.0 380.0 300.0 1 Rural 1.0
375 LP002205 Male No 1 1 No 3062 1987.0 111.0 180.0 0 Urban 0.0
376 LP002209 Female No 0 1 2764 1459.0 110.0 360.0 1 Urban 1.0
377 LP002211 Male Yes 0 1 No 4817 923.0 120.0 180.0 1 Urban 1.0
378 LP002219 Male Yes 3+ 1 No 8750 4996.0 130.0 360.0 1 Rural 1.0
379 LP002223 Male Yes 0 1 No 4310 0.0 130.0 360.0 0 Semiurban 1.0
380 LP002224 Male No 0 1 No 3069 0.0 71.0 480.0 1 Urban 0.0
381 LP002225 Male Yes 2 1 No 5391 0.0 130.0 360.0 1 Urban 1.0
382 LP002226 Male Yes 0 1 3333 2500.0 128.0 360.0 1 Semiurban 1.0
383 LP002229 Male No 0 1 No 5941 4232.0 296.0 360.0 1 Semiurban 1.0
384 LP002231 Female No 0 1 No 6000 0.0 156.0 360.0 1 Urban 1.0
385 LP002234 Male No 0 1 Yes 7167 0.0 128.0 360.0 1 Urban 1.0
386 LP002236 Male Yes 2 1 No 4566 0.0 100.0 360.0 1 Urban 0.0
387 LP002237 Male No 1 1 3667 0.0 113.0 180.0 1 Urban 1.0
388 LP002239 Male No 0 0 No 2346 1600.0 132.0 360.0 1 Semiurban 1.0
389 LP002243 Male Yes 0 0 No 3010 3136.0 0.0 360.0 0 Urban 0.0
390 LP002244 Male Yes 0 1 No 2333 2417.0 136.0 360.0 1 Urban 1.0
391 LP002250 Male Yes 0 1 No 5488 0.0 125.0 360.0 1 Rural 1.0
392 LP002255 Male No 3+ 1 No 9167 0.0 185.0 360.0 1 Rural 1.0
393 LP002262 Male Yes 3+ 1 No 9504 0.0 275.0 360.0 1 Rural 1.0
394 LP002263 Male Yes 0 1 No 2583 2115.0 120.0 360.0 0 Urban 1.0
395 LP002265 Male Yes 2 0 No 1993 1625.0 113.0 180.0 1 Semiurban 1.0
396 LP002266 Male Yes 2 1 No 3100 1400.0 113.0 360.0 1 Urban 1.0
397 LP002272 Male Yes 2 1 No 3276 484.0 135.0 360.0 0 Semiurban 1.0
398 LP002277 Female No 0 1 No 3180 0.0 71.0 360.0 0 Urban 0.0
399 LP002281 Male Yes 0 1 No 3033 1459.0 95.0 360.0 1 Urban 1.0
400 LP002284 Male No 0 0 No 3902 1666.0 109.0 360.0 1 Rural 1.0
401 LP002287 Female No 0 1 No 1500 1800.0 103.0 360.0 0 Semiurban 0.0
402 LP002288 Male Yes 2 0 No 2889 0.0 45.0 180.0 0 Urban 0.0
403 LP002296 Male No 0 0 No 2755 0.0 65.0 300.0 1 Rural 0.0
404 LP002297 Male No 0 1 No 2500 20000.0 103.0 360.0 1 Semiurban 1.0
405 LP002300 Female No 0 0 No 1963 0.0 53.0 360.0 1 Semiurban 1.0
406 LP002301 Female No 0 1 Yes 7441 0.0 194.0 360.0 1 Rural 0.0
407 LP002305 Female No 0 1 No 4547 0.0 115.0 360.0 1 Semiurban 1.0
408 LP002308 Male Yes 0 0 No 2167 2400.0 115.0 360.0 1 Urban 1.0
409 LP002314 Female No 0 0 No 2213 0.0 66.0 360.0 1 Rural 1.0
410 LP002315 Male Yes 1 1 No 8300 0.0 152.0 300.0 0 Semiurban 0.0
411 LP002317 Male Yes 3+ 1 No 81000 0.0 360.0 360.0 0 Rural 0.0
412 LP002318 Female No 1 0 Yes 3867 0.0 62.0 360.0 1 Semiurban 0.0
413 LP002319 Male Yes 0 1 6256 0.0 160.0 360.0 0 Urban 1.0
414 LP002328 Male Yes 0 0 No 6096 0.0 218.0 360.0 0 Rural 0.0
415 LP002332 Male Yes 0 0 No 2253 2033.0 110.0 360.0 1 Rural 1.0
416 LP002335 Female Yes 0 0 No 2149 3237.0 178.0 360.0 0 Semiurban 0.0
417 LP002337 Female No 0 1 No 2995 0.0 60.0 360.0 1 Urban 1.0
418 LP002341 Female No 1 1 No 2600 0.0 160.0 360.0 1 Urban 0.0
419 LP002342 Male Yes 2 1 Yes 1600 20000.0 239.0 360.0 1 Urban 0.0
420 LP002345 Male Yes 0 1 No 1025 2773.0 112.0 360.0 1 Rural 1.0
421 LP002347 Male Yes 0 1 No 3246 1417.0 138.0 360.0 1 Semiurban 1.0
422 LP002348 Male Yes 0 1 No 5829 0.0 138.0 360.0 1 Rural 1.0
423 LP002357 Female No 0 0 No 2720 0.0 80.0 0 Urban 0.0
424 LP002361 Male Yes 0 1 No 1820 1719.0 100.0 360.0 1 Urban 1.0
425 LP002362 Male Yes 1 1 No 7250 1667.0 110.0 0 Urban 0.0
426 LP002364 Male Yes 0 1 No 14880 0.0 96.0 360.0 1 Semiurban 1.0
427 LP002366 Male Yes 0 1 No 2666 4300.0 121.0 360.0 1 Rural 1.0
428 LP002367 Female No 1 0 No 4606 0.0 81.0 360.0 1 Rural 0.0
429 LP002368 Male Yes 2 1 No 5935 0.0 133.0 360.0 1 Semiurban 1.0
430 LP002369 Male Yes 0 1 No 2920 16.12000084 87.0 360.0 1 Rural 1.0
431 LP002370 Male No 0 0 No 2717 0.0 60.0 180.0 1 Urban 1.0
432 LP002377 Female No 1 1 Yes 8624 0.0 150.0 360.0 1 Semiurban 1.0
433 LP002379 Male No 0 1 No 6500 0.0 105.0 360.0 0 Rural 0.0
434 LP002386 Male No 0 1 12876 0.0 405.0 360.0 1 Semiurban 1.0
435 LP002387 Male Yes 0 1 No 2425 2340.0 143.0 360.0 1 Semiurban 1.0
436 LP002390 Male No 0 1 No 3750 0.0 100.0 360.0 1 Urban 1.0
437 LP002393 Female 1 No 10047 0.0 0.0 240.0 1 Semiurban 1.0
438 LP002398 Male No 0 1 No 1926 1851.0 50.0 360.0 1 Semiurban 1.0
439 LP002401 Male Yes 0 1 No 2213 1125.0 0.0 360.0 1 Urban 1.0
440 LP002403 Male No 0 1 Yes 10416 0.0 187.0 360.0 0 Urban 0.0
441 LP002407 Female Yes 0 0 Yes 7142 0.0 138.0 360.0 1 Rural 1.0
442 LP002408 Male No 0 1 No 3660 5064.0 187.0 360.0 1 Semiurban 1.0
443 LP002409 Male Yes 0 1 No 7901 1833.0 180.0 360.0 1 Rural 1.0
444 LP002418 Male No 3+ 0 No 4707 1993.0 148.0 360.0 1 Semiurban 1.0
445 LP002422 Male No 1 1 No 37719 0.0 152.0 360.0 1 Semiurban 1.0
446 LP002424 Male Yes 0 1 No 7333 8333.0 175.0 300.0 0 Rural 1.0
447 LP002429 Male Yes 1 1 Yes 3466 1210.0 130.0 360.0 1 Rural 1.0
448 LP002434 Male Yes 2 0 No 4652 0.0 110.0 360.0 1 Rural 1.0
449 LP002435 Male Yes 0 1 3539 1376.0 55.0 360.0 1 Rural 0.0
450 LP002443 Male Yes 2 1 No 3340 1710.0 150.0 360.0 0 Rural 0.0
451 LP002444 Male No 1 0 Yes 2769 1542.0 190.0 360.0 0 Semiurban 0.0
452 LP002446 Male Yes 2 0 No 2309 1255.0 125.0 360.0 0 Rural 0.0
453 LP002447 Male Yes 2 0 No 1958 1456.0 60.0 300.0 0 Urban 1.0
454 LP002448 Male Yes 0 1 No 3948 1733.0 149.0 360.0 0 Rural 0.0
455 LP002449 Male Yes 0 1 No 2483 2466.0 90.0 180.0 0 Rural 1.0
456 LP002453 Male No 0 1 Yes 7085 0.0 84.0 360.0 1 Semiurban 1.0
457 LP002455 Male Yes 2 1 No 3859 0.0 96.0 360.0 1 Semiurban 1.0
458 LP002459 Male Yes 0 1 No 4301 0.0 118.0 360.0 1 Urban 1.0
459 LP002467 Male Yes 0 1 No 3708 2569.0 173.0 360.0 1 Urban 0.0
460 LP002472 Male No 2 1 No 4354 0.0 136.0 360.0 1 Rural 1.0
461 LP002473 Male Yes 0 1 No 8334 0.0 160.0 360.0 1 Semiurban 0.0
462 LP002478 Yes 0 1 Yes 2083 4083.0 160.0 360.0 0 Semiurban 1.0
463 LP002484 Male Yes 3+ 1 No 7740 0.0 128.0 180.0 1 Urban 1.0
464 LP002487 Male Yes 0 1 No 3015 2188.0 153.0 360.0 1 Rural 1.0
465 LP002489 Female No 1 0 5191 0.0 132.0 360.0 1 Semiurban 1.0
466 LP002493 Male No 0 1 No 4166 0.0 98.0 360.0 0 Semiurban 0.0
467 LP002494 Male No 0 1 No 6000 0.0 140.0 360.0 1 Rural 1.0
468 LP002500 Male Yes 3+ 0 No 2947 1664.0 70.0 180.0 0 Urban 0.0
469 LP002501 Yes 0 1 No 16692 0.0 110.0 360.0 1 Semiurban 1.0
470 LP002502 Female Yes 2 0 210 2917.0 98.0 360.0 1 Semiurban 1.0
471 LP002505 Male Yes 0 1 No 4333 2451.0 110.0 360.0 1 Urban 0.0
472 LP002515 Male Yes 1 1 Yes 3450 2079.0 162.0 360.0 1 Semiurban 1.0
473 LP002517 Male Yes 1 0 No 2653 1500.0 113.0 180.0 0 Rural 0.0
474 LP002519 Male Yes 3+ 1 No 4691 0.0 100.0 360.0 1 Semiurban 1.0
475 LP002522 Female No 0 1 Yes 2500 0.0 93.0 360.0 0 Urban 1.0
476 LP002524 Male No 2 1 No 5532 4648.0 162.0 360.0 1 Rural 1.0
477 LP002527 Male Yes 2 1 Yes 16525 1014.0 150.0 360.0 1 Rural 1.0
478 LP002529 Male Yes 2 1 No 6700 1750.0 230.0 300.0 1 Semiurban 1.0
479 LP002530 Yes 2 1 No 2873 1872.0 132.0 360.0 0 Semiurban 0.0
480 LP002531 Male Yes 1 1 Yes 16667 2250.0 86.0 360.0 1 Semiurban 1.0
481 LP002533 Male Yes 2 1 No 2947 1603.0 0.0 360.0 1 Urban 0.0
482 LP002534 Female No 0 0 No 4350 0.0 154.0 360.0 1 Rural 1.0
483 LP002536 Male Yes 3+ 0 No 3095 0.0 113.0 360.0 1 Rural 1.0
484 LP002537 Male Yes 0 1 No 2083 3150.0 128.0 360.0 1 Semiurban 1.0
485 LP002541 Male Yes 0 1 No 10833 0.0 234.0 360.0 1 Semiurban 1.0
486 LP002543 Male Yes 2 1 No 8333 0.0 246.0 360.0 1 Semiurban 1.0
487 LP002544 Male Yes 1 0 No 1958 2436.0 131.0 360.0 1 Rural 1.0
488 LP002545 Male No 2 1 No 3547 0.0 80.0 360.0 0 Rural 0.0
489 LP002547 Male Yes 1 1 No 18333 0.0 500.0 360.0 1 Urban 0.0
490 LP002555 Male Yes 2 1 Yes 4583 2083.0 160.0 360.0 1 Semiurban 1.0
491 LP002556 Male No 0 1 No 2435 0.0 75.0 360.0 1 Urban 0.0
492 LP002560 Male No 0 0 No 2699 2785.0 96.0 360.0 0 Semiurban 1.0
493 LP002562 Male Yes 1 0 No 5333 1131.0 186.0 360.0 0 Urban 1.0
494 LP002571 Male No 0 0 No 3691 0.0 110.0 360.0 1 Rural 1.0
495 LP002582 Female No 0 0 Yes 17263 0.0 225.0 360.0 1 Semiurban 1.0
496 LP002585 Male Yes 0 1 No 3597 2157.0 119.0 360.0 0 Rural 0.0
497 LP002586 Female Yes 1 1 No 3326 913.0 105.0 84.0 1 Semiurban 1.0
498 LP002587 Male Yes 0 0 No 2600 1700.0 107.0 360.0 1 Rural 1.0
499 LP002588 Male Yes 0 1 No 4625 2857.0 111.0 12.0 0 Urban 1.0
500 LP002600 Male Yes 1 1 Yes 2895 0.0 95.0 360.0 1 Semiurban 1.0
501 LP002602 Male No 0 1 No 6283 4416.0 209.0 360.0 0 Rural 0.0
502 LP002603 Female No 0 1 No 645 3683.0 113.0 480.0 1 Rural 1.0
503 LP002606 Female No 0 1 No 3159 0.0 100.0 360.0 1 Semiurban 1.0
504 LP002615 Male Yes 2 1 No 4865 5624.0 208.0 360.0 1 Semiurban 1.0
505 LP002618 Male Yes 1 0 No 4050 5302.0 138.0 360.0 0 Rural 0.0
506 LP002619 Male Yes 0 0 No 3814 1483.0 124.0 300.0 1 Semiurban 1.0
507 LP002622 Male Yes 2 1 No 3510 4416.0 243.0 360.0 1 Rural 1.0
508 LP002624 Male Yes 0 1 No 20833 6667.0 480.0 360.0 0 Urban 1.0
509 LP002625 No 0 1 No 3583 0.0 96.0 360.0 1 Urban 0.0
510 LP002626 Male Yes 0 1 Yes 2479 3013.0 188.0 360.0 1 Urban 1.0
511 LP002634 Female No 1 1 No 13262 0.0 40.0 360.0 1 Urban 1.0
512 LP002637 Male No 0 0 No 3598 1287.0 100.0 360.0 1 Rural 0.0
513 LP002640 Male Yes 1 1 No 6065 2004.0 250.0 360.0 1 Semiurban 1.0
514 LP002643 Male Yes 2 1 No 3283 2035.0 148.0 360.0 1 Urban 1.0
515 LP002648 Male Yes 0 1 No 2130 6666.0 70.0 180.0 1 Semiurban 0.0
516 LP002652 Male No 0 1 No 5815 3666.0 311.0 360.0 1 Rural 0.0
517 LP002659 Male Yes 3+ 1 No 3466 3428.0 150.0 360.0 1 Rural 1.0
518 LP002670 Female Yes 2 1 No 2031 1632.0 113.0 480.0 1 Semiurban 1.0
519 LP002682 Male Yes 0 No 3074 1800.0 123.0 360.0 0 Semiurban 0.0
520 LP002683 Male No 0 1 No 4683 1915.0 185.0 360.0 1 Semiurban 0.0
521 LP002684 Female No 0 0 No 3400 0.0 95.0 360.0 1 Rural 0.0
522 LP002689 Male Yes 2 0 No 2192 1742.0 45.0 360.0 1 Semiurban 1.0
523 LP002690 Male No 0 1 No 2500 0.0 55.0 360.0 1 Semiurban 1.0
524 LP002692 Male Yes 3+ 1 Yes 5677 1424.0 100.0 360.0 1 Rural 1.0
525 LP002693 Male Yes 2 1 Yes 7948 7166.0 480.0 360.0 1 Rural 1.0
526 LP002697 Male No 0 1 No 4680 2087.0 0.0 360.0 1 Semiurban 0.0
527 LP002699 Male Yes 2 1 Yes 17500 0.0 400.0 360.0 1 Rural 1.0
528 LP002705 Male Yes 0 1 No 3775 0.0 110.0 360.0 1 Semiurban 1.0
529 LP002706 Male Yes 1 0 No 5285 1430.0 161.0 360.0 0 Semiurban 1.0
530 LP002714 Male No 1 0 No 2679 1302.0 94.0 360.0 1 Semiurban 1.0
531 LP002716 Male No 0 0 No 6783 0.0 130.0 360.0 1 Semiurban 1.0
532 LP002717 Male Yes 0 1 No 1025 5500.0 216.0 360.0 0 Rural 1.0
533 LP002720 Male Yes 3+ 1 No 4281 0.0 100.0 360.0 1 Urban 1.0
534 LP002723 Male No 2 1 No 3588 0.0 110.0 360.0 0 Rural 0.0
535 LP002729 Male No 1 1 No 11250 0.0 196.0 360.0 0 Semiurban 0.0
536 LP002731 Female No 0 0 Yes 18165 0.0 125.0 360.0 1 Urban 1.0
537 LP002732 Male No 0 0 2550 2042.0 126.0 360.0 1 Rural 1.0
538 LP002734 Male Yes 0 1 No 6133 3906.0 324.0 360.0 1 Urban 1.0
539 LP002738 Male No 2 1 No 3617 0.0 107.0 360.0 1 Semiurban 1.0
540 LP002739 Male Yes 0 0 No 2917 536.0 66.0 360.0 1 Rural 0.0
541 LP002740 Male Yes 3+ 1 No 6417 0.0 157.0 180.0 1 Rural 1.0
542 LP002741 Female Yes 1 1 No 4608 2845.0 140.0 180.0 1 Semiurban 1.0
543 LP002743 Female No 0 1 No 2138 0.0 99.0 360.0 0 Semiurban 0.0
544 LP002753 Female No 1 1 3652 0.0 95.0 360.0 1 Semiurban 1.0
545 LP002755 Male Yes 1 0 No 2239 2524.0 128.0 360.0 1 Urban 1.0
546 LP002757 Female Yes 0 0 No 3017 663.0 102.0 360.0 0 Semiurban 1.0
547 LP002767 Male Yes 0 1 No 2768 1950.0 155.0 360.0 1 Rural 1.0
548 LP002768 Male No 0 0 No 3358 0.0 80.0 36.0 1 Semiurban 0.0
549 LP002772 Male No 0 1 No 2526 1783.0 145.0 360.0 1 Rural 1.0
550 LP002776 Female No 0 1 No 5000 0.0 103.0 360.0 0 Semiurban 0.0
551 LP002777 Male Yes 0 1 No 2785 2016.0 110.0 360.0 1 Rural 1.0
552 LP002778 Male Yes 2 1 Yes 6633 0.0 0.0 360.0 0 Rural 0.0
553 LP002784 Male Yes 1 0 No 2492 2375.0 0.0 360.0 1 Rural 1.0
554 LP002785 Male Yes 1 1 No 3333 3250.0 158.0 360.0 1 Urban 1.0
555 LP002788 Male Yes 0 0 No 2454 2333.0 181.0 360.0 0 Urban 0.0
556 LP002789 Male Yes 0 1 No 3593 4266.0 132.0 180.0 0 Rural 0.0
557 LP002792 Male Yes 1 1 No 5468 1032.0 26.0 360.0 1 Semiurban 1.0
558 LP002794 Female No 0 1 No 2667 1625.0 84.0 360.0 0 Urban 1.0
559 LP002795 Male Yes 3+ 1 Yes 10139 0.0 260.0 360.0 1 Semiurban 1.0
560 LP002798 Male Yes 0 1 No 3887 2669.0 162.0 360.0 1 Semiurban 1.0
561 LP002804 Female Yes 0 1 No 4180 2306.0 182.0 360.0 1 Semiurban 1.0
562 LP002807 Male Yes 2 0 No 3675 242.0 108.0 360.0 1 Semiurban 1.0
563 LP002813 Female Yes 1 1 Yes 19484 0.0 600.0 360.0 1 Semiurban 1.0
564 LP002820 Male Yes 0 1 No 5923 2054.0 211.0 360.0 1 Rural 1.0
565 LP002821 Male No 0 0 Yes 5800 0.0 132.0 360.0 1 Semiurban 1.0
566 LP002832 Male Yes 2 1 No 8799 0.0 258.0 360.0 0 Urban 0.0
567 LP002833 Male Yes 0 0 No 4467 0.0 120.0 360.0 0 Rural 1.0
568 LP002836 Male No 0 1 No 3333 0.0 70.0 360.0 1 Urban 1.0
569 LP002837 Male Yes 3+ 1 No 3400 2500.0 123.0 360.0 0 Rural 0.0
570 LP002840 Female No 0 1 No 2378 0.0 9.0 360.0 1 Urban 0.0
571 LP002841 Male Yes 0 1 No 3166 2064.0 104.0 360.0 0 Urban 0.0
572 LP002842 Male Yes 1 1 No 3417 1750.0 186.0 360.0 1 Urban 1.0
573 LP002847 Male Yes 1 No 5116 1451.0 165.0 360.0 0 Urban 0.0
574 LP002855 Male Yes 2 1 No 16666 0.0 275.0 360.0 1 Urban 1.0
575 LP002862 Male Yes 2 0 No 6125 1625.0 187.0 480.0 1 Semiurban 0.0
576 LP002863 Male Yes 3+ 1 No 6406 0.0 150.0 360.0 1 Semiurban 0.0
577 LP002868 Male Yes 2 1 No 3159 461.0 108.0 84.0 1 Urban 1.0
578 LP002872 Yes 0 1 No 3087 2210.0 136.0 360.0 0 Semiurban 0.0
579 LP002874 Male No 0 1 No 3229 2739.0 110.0 360.0 1 Urban 1.0
580 LP002877 Male Yes 1 1 No 1782 2232.0 107.0 360.0 1 Rural 1.0
581 LP002888 Male No 0 1 3182 2917.0 161.0 360.0 1 Urban 1.0
582 LP002892 Male Yes 2 1 No 6540 0.0 205.0 360.0 1 Semiurban 1.0
583 LP002893 Male No 0 1 No 1836 33837.0 90.0 360.0 1 Urban 0.0
584 LP002894 Female Yes 0 1 No 3166 0.0 36.0 360.0 1 Semiurban 1.0
585 LP002898 Male Yes 1 1 No 1880 0.0 61.0 360.0 0 Rural 0.0
586 LP002911 Male Yes 1 1 No 2787 1917.0 146.0 360.0 0 Rural 0.0
587 LP002912 Male Yes 1 1 No 4283 3000.0 172.0 84.0 1 Rural 0.0
588 LP002916 Male Yes 0 1 No 2297 1522.0 104.0 360.0 1 Urban 1.0
589 LP002917 Female No 0 0 No 2165 0.0 70.0 360.0 1 Semiurban 1.0
590 LP002925 No 0 1 No 4750 0.0 94.0 360.0 1 Semiurban 1.0
591 LP002926 Male Yes 2 1 Yes 2726 0.0 106.0 360.0 0 Semiurban 0.0
592 LP002928 Male Yes 0 1 No 3000 3416.0 56.0 180.0 1 Semiurban 1.0
593 LP002931 Male Yes 2 1 Yes 6000 0.0 205.0 240.0 1 Semiurban 0.0
594 LP002933 No 3+ 1 Yes 9357 0.0 292.0 360.0 1 Semiurban 1.0
595 LP002936 Male Yes 0 1 No 3859 3300.0 142.0 180.0 1 Rural 1.0
596 LP002938 Male Yes 0 1 Yes 16120 0.0 260.0 360.0 1 Urban 1.0
597 LP002940 Male No 0 0 No 3833 0.0 110.0 360.0 1 Rural 1.0
598 LP002941 Male Yes 2 0 Yes 6383 1000.0 187.0 360.0 1 Rural 0.0
599 LP002943 Male No 1 No 2987 0.0 88.0 360.0 0 Semiurban 0.0
600 LP002945 Male Yes 0 1 Yes 9963 0.0 180.0 360.0 1 Rural 1.0
601 LP002948 Male Yes 2 1 No 5780 0.0 192.0 360.0 1 Urban 1.0
602 LP002949 Female No 3+ 1 416 41667.0 350.0 180.0 0 Urban 0.0
603 LP002950 Male Yes 0 0 2894 2792.0 155.0 360.0 1 Rural 1.0
604 LP002953 Male Yes 3+ 1 No 5703 0.0 128.0 360.0 1 Urban 1.0
605 LP002958 Male No 0 1 No 3676 4301.0 172.0 360.0 1 Rural 1.0
606 LP002959 Female Yes 1 1 No 12000 0.0 496.0 360.0 1 Semiurban 1.0
607 LP002960 Male Yes 0 0 No 2400 3800.0 0.0 180.0 1 Urban 0.0
608 LP002961 Male Yes 1 1 No 3400 2500.0 173.0 360.0 1 Semiurban 1.0
609 LP002964 Male Yes 2 0 No 3987 1411.0 157.0 360.0 1 Rural 1.0
610 LP002974 Male Yes 0 1 No 3232 1950.0 108.0 360.0 1 Rural 1.0
611 LP002978 Female No 0 1 No 2900 0.0 71.0 360.0 1 Rural 1.0
612 LP002979 Male Yes 3+ 1 No 4106 0.0 40.0 180.0 1 Rural 1.0
613 LP002983 Male Yes 1 1 No 8072 240.0 253.0 360.0 1 Urban 1.0
614 LP002984 Male Yes 2 1 No 7583 0.0 187.0 360.0 1 Urban 1.0
615 LP002990 Female No 0 1 Yes 4583 0.0 133.0 360.0 0 Semiurban 0.0

BIN
abanin_daniil_lab_3/result.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 27 KiB

26
abanin_daniil_lab_4/README.md Normal file
View File

@ -0,0 +1,26 @@
## Лабораторная работа №4
### Ранжирование признаков
## ПИбд-41 Абанин Даниил
### Как запустить лабораторную работу:
* установить python, pandas, matplotlib, scipy
* запустить проект (стартовая точка lab4)
### Какие технологии использовались:
* Язык программирования `Python`, библиотеки pandas, matplotlib, scipy
* Среда разработки `PyCharm`
### Что делает лабораторная работа:
Программа читает данные из csv файла. На основе имеющейся информации кластеризует заявителей на различные группы по риску выдачи кредита.
При кластеризации используются такие признаки, как: ApplicantIncome - доход заявителя, LoanAmount - сумма займа в тысячах, Credit_History -
статус кредитной истории заявителя (соответствие рекомендациям), Self_Employed - самозанятость (Да/Нет), Education - наличие образования
### Тест
![Result](result.png)
По результатам кластеризации дендрограммой видно, что было проведено эффективное разбиение данных. На диаграмме показаны различные группы заявителей по рискам выдачи кредита

23
abanin_daniil_lab_4/lab4.py Normal file
View File

@ -0,0 +1,23 @@
from scipy.cluster import hierarchy
import pandas as pd
from matplotlib import pyplot as plt
def start():
data = pd.read_csv('loan.csv')
x = data[['ApplicantIncome', 'LoanAmount', 'Credit_History', 'Self_Employed', 'Education']]
plt.figure(1, figsize=(16, 9))
plt.title('Дендрограмма кластеризации заявителей')
hierarchy.dendrogram(hierarchy.linkage(x, method='single'),
truncate_mode='lastp',
p=20,
orientation='top',
leaf_rotation=90,
leaf_font_size=8,
show_contracted=True)
plt.show()
start()

615
abanin_daniil_lab_4/loan.csv Normal file
View File

@ -0,0 +1,615 @@
Loan_ID,Gender,Married,Dependents,Education,Self_Employed,ApplicantIncome,CoapplicantIncome,LoanAmount,Loan_Amount_Term,Credit_History,Property_Area,Loan_Status
LP001002,Male,No,0,1,0.0,5849,0.0,360.0,1.0,0,Y,0.0
LP001003,Male,Yes,1,1,0.0,4583,1508.0,128.0,360.0,1,Rural,0.0
LP001005,Male,Yes,0,1,1.0,3000,0.0,66.0,360.0,1,Urban,1.0
LP001006,Male,Yes,0,0,0.0,2583,2358.0,120.0,360.0,1,Urban,1.0
LP001008,Male,No,0,1,0.0,6000,0.0,141.0,360.0,1,Urban,1.0
LP001011,Male,Yes,2,1,1.0,5417,4196.0,267.0,360.0,1,Urban,1.0
LP001013,Male,Yes,0,0,0.0,2333,1516.0,95.0,360.0,1,Urban,1.0
LP001014,Male,Yes,3+,1,0.0,3036,2504.0,158.0,360.0,0,Semiurban,0.0
LP001018,Male,Yes,2,1,0.0,4006,1526.0,168.0,360.0,1,Urban,1.0
LP001020,Male,Yes,1,1,0.0,12841,10968.0,349.0,360.0,1,Semiurban,0.0
LP001024,Male,Yes,2,1,0.0,3200,700.0,70.0,360.0,1,Urban,1.0
LP001027,Male,Yes,2,1,0.0,2500,1840.0,109.0,360.0,1,Urban,1.0
LP001028,Male,Yes,2,1,0.0,3073,8106.0,200.0,360.0,1,Urban,1.0
LP001029,Male,No,0,1,0.0,1853,2840.0,114.0,360.0,1,Rural,0.0
LP001030,Male,Yes,2,1,0.0,1299,1086.0,17.0,120.0,1,Urban,1.0
LP001032,Male,No,0,1,0.0,4950,0.0,125.0,360.0,1,Urban,1.0
LP001034,Male,No,1,0,0.0,3596,0.0,100.0,240.0,0,Urban,1.0
LP001036,Female,No,0,1,0.0,3510,0.0,76.0,360.0,0,Urban,0.0
LP001038,Male,Yes,0,0,0.0,4887,0.0,133.0,360.0,1,Rural,0.0
LP001041,Male,Yes,0,1,0.0,2600,3500.0,115.0,,1,Urban,1.0
LP001043,Male,Yes,0,0,0.0,7660,0.0,104.0,360.0,0,Urban,0.0
LP001046,Male,Yes,1,1,0.0,5955,5625.0,315.0,360.0,1,Urban,1.0
LP001047,Male,Yes,0,0,0.0,2600,1911.0,116.0,360.0,0,Semiurban,0.0
LP001050,,Yes,2,0,0.0,3365,1917.0,112.0,360.0,0,Rural,0.0
LP001052,Male,Yes,1,1,0.0,3717,2925.0,151.0,360.0,0,Semiurban,0.0
LP001066,Male,Yes,0,1,1.0,9560,0.0,191.0,360.0,1,Semiurban,1.0
LP001068,Male,Yes,0,1,0.0,2799,2253.0,122.0,360.0,1,Semiurban,1.0
LP001073,Male,Yes,2,0,0.0,4226,1040.0,110.0,360.0,1,Urban,1.0
LP001086,Male,No,0,0,0.0,1442,0.0,35.0,360.0,1,Urban,0.0
LP001087,Female,No,2,1,0.0,3750,2083.0,120.0,360.0,1,Semiurban,1.0
LP001091,Male,Yes,1,1,0.0,4166,3369.0,201.0,360.0,0,Urban,0.0
LP001095,Male,No,0,1,0.0,3167,0.0,74.0,360.0,1,Urban,0.0
LP001097,Male,No,1,1,1.0,4692,0.0,106.0,360.0,1,Rural,0.0
LP001098,Male,Yes,0,1,0.0,3500,1667.0,114.0,360.0,1,Semiurban,1.0
LP001100,Male,No,3+,1,0.0,12500,3000.0,320.0,360.0,1,Rural,0.0
LP001106,Male,Yes,0,1,0.0,2275,2067.0,0.0,360.0,1,Urban,1.0
LP001109,Male,Yes,0,1,0.0,1828,1330.0,100.0,,0,Urban,0.0
LP001112,Female,Yes,0,1,0.0,3667,1459.0,144.0,360.0,1,Semiurban,1.0
LP001114,Male,No,0,1,0.0,4166,7210.0,184.0,360.0,1,Urban,1.0
LP001116,Male,No,0,0,0.0,3748,1668.0,110.0,360.0,1,Semiurban,1.0
LP001119,Male,No,0,1,0.0,3600,0.0,80.0,360.0,1,Urban,0.0
LP001120,Male,No,0,1,0.0,1800,1213.0,47.0,360.0,1,Urban,1.0
LP001123,Male,Yes,0,1,0.0,2400,0.0,75.0,360.0,0,Urban,1.0
LP001131,Male,Yes,0,1,0.0,3941,2336.0,134.0,360.0,1,Semiurban,1.0
LP001136,Male,Yes,0,0,1.0,4695,0.0,96.0,,1,Urban,1.0
LP001137,Female,No,0,1,0.0,3410,0.0,88.0,,1,Urban,1.0
LP001138,Male,Yes,1,1,0.0,5649,0.0,44.0,360.0,1,Urban,1.0
LP001144,Male,Yes,0,1,0.0,5821,0.0,144.0,360.0,1,Urban,1.0
LP001146,Female,Yes,0,1,0.0,2645,3440.0,120.0,360.0,0,Urban,0.0
LP001151,Female,No,0,1,0.0,4000,2275.0,144.0,360.0,1,Semiurban,1.0
LP001155,Female,Yes,0,0,0.0,1928,1644.0,100.0,360.0,1,Semiurban,1.0
LP001157,Female,No,0,1,0.0,3086,0.0,120.0,360.0,1,Semiurban,1.0
LP001164,Female,No,0,1,0.0,4230,0.0,112.0,360.0,1,Semiurban,0.0
LP001179,Male,Yes,2,1,0.0,4616,0.0,134.0,360.0,1,Urban,0.0
LP001186,Female,Yes,1,1,1.0,11500,0.0,286.0,360.0,0,Urban,0.0
LP001194,Male,Yes,2,1,0.0,2708,1167.0,97.0,360.0,1,Semiurban,1.0
LP001195,Male,Yes,0,1,0.0,2132,1591.0,96.0,360.0,1,Semiurban,1.0
LP001197,Male,Yes,0,1,0.0,3366,2200.0,135.0,360.0,1,Rural,0.0
LP001198,Male,Yes,1,1,0.0,8080,2250.0,180.0,360.0,1,Urban,1.0
LP001199,Male,Yes,2,0,0.0,3357,2859.0,144.0,360.0,1,Urban,1.0
LP001205,Male,Yes,0,1,0.0,2500,3796.0,120.0,360.0,1,Urban,1.0
LP001206,Male,Yes,3+,1,0.0,3029,0.0,99.0,360.0,1,Urban,1.0
LP001207,Male,Yes,0,0,1.0,2609,3449.0,165.0,180.0,0,Rural,0.0
LP001213,Male,Yes,1,1,0.0,4945,0.0,0.0,360.0,0,Rural,0.0
LP001222,Female,No,0,1,0.0,4166,0.0,116.0,360.0,0,Semiurban,0.0
LP001225,Male,Yes,0,1,0.0,5726,4595.0,258.0,360.0,1,Semiurban,0.0
LP001228,Male,No,0,0,0.0,3200,2254.0,126.0,180.0,0,Urban,0.0
LP001233,Male,Yes,1,1,0.0,10750,0.0,312.0,360.0,1,Urban,1.0
LP001238,Male,Yes,3+,0,1.0,7100,0.0,125.0,60.0,1,Urban,1.0
LP001241,Female,No,0,1,0.0,4300,0.0,136.0,360.0,0,Semiurban,0.0
LP001243,Male,Yes,0,1,0.0,3208,3066.0,172.0,360.0,1,Urban,1.0
LP001245,Male,Yes,2,0,1.0,1875,1875.0,97.0,360.0,1,Semiurban,1.0
LP001248,Male,No,0,1,0.0,3500,0.0,81.0,300.0,1,Semiurban,1.0
LP001250,Male,Yes,3+,0,0.0,4755,0.0,95.0,,0,Semiurban,0.0
LP001253,Male,Yes,3+,1,1.0,5266,1774.0,187.0,360.0,1,Semiurban,1.0
LP001255,Male,No,0,1,0.0,3750,0.0,113.0,480.0,1,Urban,0.0
LP001256,Male,No,0,1,0.0,3750,4750.0,176.0,360.0,1,Urban,0.0
LP001259,Male,Yes,1,1,1.0,1000,3022.0,110.0,360.0,1,Urban,0.0
LP001263,Male,Yes,3+,1,0.0,3167,4000.0,180.0,300.0,0,Semiurban,0.0
LP001264,Male,Yes,3+,0,1.0,3333,2166.0,130.0,360.0,0,Semiurban,1.0
LP001265,Female,No,0,1,0.0,3846,0.0,111.0,360.0,1,Semiurban,1.0
LP001266,Male,Yes,1,1,1.0,2395,0.0,0.0,360.0,1,Semiurban,1.0
LP001267,Female,Yes,2,1,0.0,1378,1881.0,167.0,360.0,1,Urban,0.0
LP001273,Male,Yes,0,1,0.0,6000,2250.0,265.0,360.0,0,Semiurban,0.0
LP001275,Male,Yes,1,1,0.0,3988,0.0,50.0,240.0,1,Urban,1.0
LP001279,Male,No,0,1,0.0,2366,2531.0,136.0,360.0,1,Semiurban,1.0
LP001280,Male,Yes,2,0,0.0,3333,2000.0,99.0,360.0,0,Semiurban,1.0
LP001282,Male,Yes,0,1,0.0,2500,2118.0,104.0,360.0,1,Semiurban,1.0
LP001289,Male,No,0,1,0.0,8566,0.0,210.0,360.0,1,Urban,1.0
LP001310,Male,Yes,0,1,0.0,5695,4167.0,175.0,360.0,1,Semiurban,1.0
LP001316,Male,Yes,0,1,0.0,2958,2900.0,131.0,360.0,1,Semiurban,1.0
LP001318,Male,Yes,2,1,0.0,6250,5654.0,188.0,180.0,1,Semiurban,1.0
LP001319,Male,Yes,2,0,0.0,3273,1820.0,81.0,360.0,1,Urban,1.0
LP001322,Male,No,0,1,0.0,4133,0.0,122.0,360.0,1,Semiurban,1.0
LP001325,Male,No,0,0,0.0,3620,0.0,25.0,120.0,1,Semiurban,1.0
LP001326,Male,No,0,1,0.0,6782,0.0,0.0,360.0,0,Urban,0.0
LP001327,Female,Yes,0,1,0.0,2484,2302.0,137.0,360.0,1,Semiurban,1.0
LP001333,Male,Yes,0,1,0.0,1977,997.0,50.0,360.0,1,Semiurban,1.0
LP001334,Male,Yes,0,0,0.0,4188,0.0,115.0,180.0,1,Semiurban,1.0
LP001343,Male,Yes,0,1,0.0,1759,3541.0,131.0,360.0,1,Semiurban,1.0
LP001345,Male,Yes,2,0,0.0,4288,3263.0,133.0,180.0,1,Urban,1.0
LP001349,Male,No,0,1,0.0,4843,3806.0,151.0,360.0,1,Semiurban,1.0
LP001350,Male,Yes,,1,0.0,13650,0.0,0.0,360.0,1,Urban,1.0
LP001356,Male,Yes,0,1,0.0,4652,3583.0,0.0,360.0,1,Semiurban,1.0
LP001357,Male,,,1,0.0,3816,754.0,160.0,360.0,1,Urban,1.0
LP001367,Male,Yes,1,1,0.0,3052,1030.0,100.0,360.0,1,Urban,1.0
LP001369,Male,Yes,2,1,0.0,11417,1126.0,225.0,360.0,1,Urban,1.0
LP001370,Male,No,0,0,0.0,7333,0.0,120.0,360.0,1,Rural,0.0
LP001379,Male,Yes,2,1,0.0,3800,3600.0,216.0,360.0,0,Urban,0.0
LP001384,Male,Yes,3+,0,0.0,2071,754.0,94.0,480.0,1,Semiurban,1.0
LP001385,Male,No,0,1,0.0,5316,0.0,136.0,360.0,1,Urban,1.0
LP001387,Female,Yes,0,1,0.0,2929,2333.0,139.0,360.0,1,Semiurban,1.0
LP001391,Male,Yes,0,0,0.0,3572,4114.0,152.0,,0,Rural,0.0
LP001392,Female,No,1,1,1.0,7451,0.0,0.0,360.0,1,Semiurban,1.0
LP001398,Male,No,0,1,0.0,5050,0.0,118.0,360.0,1,Semiurban,1.0
LP001401,Male,Yes,1,1,0.0,14583,0.0,185.0,180.0,1,Rural,1.0
LP001404,Female,Yes,0,1,0.0,3167,2283.0,154.0,360.0,1,Semiurban,1.0
LP001405,Male,Yes,1,1,0.0,2214,1398.0,85.0,360.0,0,Urban,1.0
LP001421,Male,Yes,0,1,0.0,5568,2142.0,175.0,360.0,1,Rural,0.0
LP001422,Female,No,0,1,0.0,10408,0.0,259.0,360.0,1,Urban,1.0
LP001426,Male,Yes,,1,0.0,5667,2667.0,180.0,360.0,1,Rural,1.0
LP001430,Female,No,0,1,0.0,4166,0.0,44.0,360.0,1,Semiurban,1.0
LP001431,Female,No,0,1,0.0,2137,8980.0,137.0,360.0,0,Semiurban,1.0
LP001432,Male,Yes,2,1,0.0,2957,0.0,81.0,360.0,1,Semiurban,1.0
LP001439,Male,Yes,0,0,0.0,4300,2014.0,194.0,360.0,1,Rural,1.0
LP001443,Female,No,0,1,0.0,3692,0.0,93.0,360.0,0,Rural,1.0
LP001448,,Yes,3+,1,0.0,23803,0.0,370.0,360.0,1,Rural,1.0
LP001449,Male,No,0,1,0.0,3865,1640.0,0.0,360.0,1,Rural,1.0
LP001451,Male,Yes,1,1,1.0,10513,3850.0,160.0,180.0,0,Urban,0.0
LP001465,Male,Yes,0,1,0.0,6080,2569.0,182.0,360.0,0,Rural,0.0
LP001469,Male,No,0,1,1.0,20166,0.0,650.0,480.0,0,Urban,1.0
LP001473,Male,No,0,1,0.0,2014,1929.0,74.0,360.0,1,Urban,1.0
LP001478,Male,No,0,1,0.0,2718,0.0,70.0,360.0,1,Semiurban,1.0
LP001482,Male,Yes,0,1,1.0,3459,0.0,25.0,120.0,1,Semiurban,1.0
LP001487,Male,No,0,1,0.0,4895,0.0,102.0,360.0,1,Semiurban,1.0
LP001488,Male,Yes,3+,1,0.0,4000,7750.0,290.0,360.0,1,Semiurban,0.0
LP001489,Female,Yes,0,1,0.0,4583,0.0,84.0,360.0,1,Rural,0.0
LP001491,Male,Yes,2,1,1.0,3316,3500.0,88.0,360.0,1,Urban,1.0
LP001492,Male,No,0,1,0.0,14999,0.0,242.0,360.0,0,Semiurban,0.0
LP001493,Male,Yes,2,0,0.0,4200,1430.0,129.0,360.0,1,Rural,0.0
LP001497,Male,Yes,2,1,0.0,5042,2083.0,185.0,360.0,1,Rural,0.0
LP001498,Male,No,0,1,0.0,5417,0.0,168.0,360.0,1,Urban,1.0
LP001504,Male,No,0,1,1.0,6950,0.0,175.0,180.0,1,Semiurban,1.0
LP001507,Male,Yes,0,1,0.0,2698,2034.0,122.0,360.0,1,Semiurban,1.0
LP001508,Male,Yes,2,1,0.0,11757,0.0,187.0,180.0,1,Urban,1.0
LP001514,Female,Yes,0,1,0.0,2330,4486.0,100.0,360.0,1,Semiurban,1.0
LP001516,Female,Yes,2,1,0.0,14866,0.0,70.0,360.0,1,Urban,1.0
LP001518,Male,Yes,1,1,0.0,1538,1425.0,30.0,360.0,1,Urban,1.0
LP001519,Female,No,0,1,0.0,10000,1666.0,225.0,360.0,1,Rural,0.0
LP001520,Male,Yes,0,1,0.0,4860,830.0,125.0,360.0,1,Semiurban,1.0
LP001528,Male,No,0,1,0.0,6277,0.0,118.0,360.0,0,Rural,0.0
LP001529,Male,Yes,0,1,1.0,2577,3750.0,152.0,360.0,1,Rural,1.0
LP001531,Male,No,0,1,0.0,9166,0.0,244.0,360.0,1,Urban,0.0
LP001532,Male,Yes,2,0,0.0,2281,0.0,113.0,360.0,1,Rural,0.0
LP001535,Male,No,0,1,0.0,3254,0.0,50.0,360.0,1,Urban,1.0
LP001536,Male,Yes,3+,1,0.0,39999,0.0,600.0,180.0,0,Semiurban,1.0
LP001541,Male,Yes,1,1,0.0,6000,0.0,160.0,360.0,0,Rural,1.0
LP001543,Male,Yes,1,1,0.0,9538,0.0,187.0,360.0,1,Urban,1.0
LP001546,Male,No,0,1,0.0,2980,2083.0,120.0,360.0,1,Rural,1.0
LP001552,Male,Yes,0,1,0.0,4583,5625.0,255.0,360.0,1,Semiurban,1.0
LP001560,Male,Yes,0,0,0.0,1863,1041.0,98.0,360.0,1,Semiurban,1.0
LP001562,Male,Yes,0,1,0.0,7933,0.0,275.0,360.0,1,Urban,0.0
LP001565,Male,Yes,1,1,0.0,3089,1280.0,121.0,360.0,0,Semiurban,0.0
LP001570,Male,Yes,2,1,0.0,4167,1447.0,158.0,360.0,1,Rural,1.0
LP001572,Male,Yes,0,1,0.0,9323,0.0,75.0,180.0,1,Urban,1.0
LP001574,Male,Yes,0,1,0.0,3707,3166.0,182.0,,1,Rural,1.0
LP001577,Female,Yes,0,1,0.0,4583,0.0,112.0,360.0,1,Rural,0.0
LP001578,Male,Yes,0,1,0.0,2439,3333.0,129.0,360.0,1,Rural,1.0
LP001579,Male,No,0,1,0.0,2237,0.0,63.0,480.0,0,Semiurban,0.0
LP001580,Male,Yes,2,1,0.0,8000,0.0,200.0,360.0,1,Semiurban,1.0
LP001581,Male,Yes,0,0,0.0,1820,1769.0,95.0,360.0,1,Rural,1.0
LP001585,,Yes,3+,1,0.0,51763,0.0,700.0,300.0,1,Urban,1.0
LP001586,Male,Yes,3+,0,0.0,3522,0.0,81.0,180.0,1,Rural,0.0
LP001594,Male,Yes,0,1,0.0,5708,5625.0,187.0,360.0,1,Semiurban,1.0
LP001603,Male,Yes,0,0,1.0,4344,736.0,87.0,360.0,1,Semiurban,0.0
LP001606,Male,Yes,0,1,0.0,3497,1964.0,116.0,360.0,1,Rural,1.0
LP001608,Male,Yes,2,1,0.0,2045,1619.0,101.0,360.0,1,Rural,1.0
LP001610,Male,Yes,3+,1,0.0,5516,11300.0,495.0,360.0,0,Semiurban,0.0
LP001616,Male,Yes,1,1,0.0,3750,0.0,116.0,360.0,1,Semiurban,1.0
LP001630,Male,No,0,0,0.0,2333,1451.0,102.0,480.0,0,Urban,0.0
LP001633,Male,Yes,1,1,0.0,6400,7250.0,180.0,360.0,0,Urban,0.0
LP001634,Male,No,0,1,0.0,1916,5063.0,67.0,360.0,0,Rural,0.0
LP001636,Male,Yes,0,1,0.0,4600,0.0,73.0,180.0,1,Semiurban,1.0
LP001637,Male,Yes,1,1,0.0,33846,0.0,260.0,360.0,1,Semiurban,0.0
LP001639,Female,Yes,0,1,0.0,3625,0.0,108.0,360.0,1,Semiurban,1.0
LP001640,Male,Yes,0,1,1.0,39147,4750.0,120.0,360.0,1,Semiurban,1.0
LP001641,Male,Yes,1,1,1.0,2178,0.0,66.0,300.0,0,Rural,0.0
LP001643,Male,Yes,0,1,0.0,2383,2138.0,58.0,360.0,0,Rural,1.0
LP001644,,Yes,0,1,1.0,674,5296.0,168.0,360.0,1,Rural,1.0
LP001647,Male,Yes,0,1,0.0,9328,0.0,188.0,180.0,1,Rural,1.0
LP001653,Male,No,0,0,0.0,4885,0.0,48.0,360.0,1,Rural,1.0
LP001656,Male,No,0,1,0.0,12000,0.0,164.0,360.0,1,Semiurban,0.0
LP001657,Male,Yes,0,0,0.0,6033,0.0,160.0,360.0,1,Urban,0.0
LP001658,Male,No,0,1,0.0,3858,0.0,76.0,360.0,1,Semiurban,1.0
LP001664,Male,No,0,1,0.0,4191,0.0,120.0,360.0,1,Rural,1.0
LP001665,Male,Yes,1,1,0.0,3125,2583.0,170.0,360.0,1,Semiurban,0.0
LP001666,Male,No,0,1,0.0,8333,3750.0,187.0,360.0,1,Rural,1.0
LP001669,Female,No,0,0,0.0,1907,2365.0,120.0,,1,Urban,1.0
LP001671,Female,Yes,0,1,0.0,3416,2816.0,113.0,360.0,0,Semiurban,1.0
LP001673,Male,No,0,1,1.0,11000,0.0,83.0,360.0,1,Urban,0.0
LP001674,Male,Yes,1,0,0.0,2600,2500.0,90.0,360.0,1,Semiurban,1.0
LP001677,Male,No,2,1,0.0,4923,0.0,166.0,360.0,0,Semiurban,1.0
LP001682,Male,Yes,3+,0,0.0,3992,0.0,0.0,180.0,1,Urban,0.0
LP001688,Male,Yes,1,0,0.0,3500,1083.0,135.0,360.0,1,Urban,1.0
LP001691,Male,Yes,2,0,0.0,3917,0.0,124.0,360.0,1,Semiurban,1.0
LP001692,Female,No,0,0,0.0,4408,0.0,120.0,360.0,1,Semiurban,1.0
LP001693,Female,No,0,1,0.0,3244,0.0,80.0,360.0,1,Urban,1.0
LP001698,Male,No,0,0,0.0,3975,2531.0,55.0,360.0,1,Rural,1.0
LP001699,Male,No,0,1,0.0,2479,0.0,59.0,360.0,1,Urban,1.0
LP001702,Male,No,0,1,0.0,3418,0.0,127.0,360.0,1,Semiurban,0.0
LP001708,Female,No,0,1,0.0,10000,0.0,214.0,360.0,1,Semiurban,0.0
LP001711,Male,Yes,3+,1,0.0,3430,1250.0,128.0,360.0,0,Semiurban,0.0
LP001713,Male,Yes,1,1,1.0,7787,0.0,240.0,360.0,1,Urban,1.0
LP001715,Male,Yes,3+,0,1.0,5703,0.0,130.0,360.0,1,Rural,1.0
LP001716,Male,Yes,0,1,0.0,3173,3021.0,137.0,360.0,1,Urban,1.0
LP001720,Male,Yes,3+,0,0.0,3850,983.0,100.0,360.0,1,Semiurban,1.0
LP001722,Male,Yes,0,1,0.0,150,1800.0,135.0,360.0,1,Rural,0.0
LP001726,Male,Yes,0,1,0.0,3727,1775.0,131.0,360.0,1,Semiurban,1.0
LP001732,Male,Yes,2,1,0.0,5000,0.0,72.0,360.0,0,Semiurban,0.0
LP001734,Female,Yes,2,1,0.0,4283,2383.0,127.0,360.0,0,Semiurban,1.0
LP001736,Male,Yes,0,1,0.0,2221,0.0,60.0,360.0,0,Urban,0.0
LP001743,Male,Yes,2,1,0.0,4009,1717.0,116.0,360.0,1,Semiurban,1.0
LP001744,Male,No,0,1,0.0,2971,2791.0,144.0,360.0,1,Semiurban,1.0
LP001749,Male,Yes,0,1,0.0,7578,1010.0,175.0,,1,Semiurban,1.0
LP001750,Male,Yes,0,1,0.0,6250,0.0,128.0,360.0,1,Semiurban,1.0
LP001751,Male,Yes,0,1,0.0,3250,0.0,170.0,360.0,1,Rural,0.0
LP001754,Male,Yes,,0,1.0,4735,0.0,138.0,360.0,1,Urban,0.0
LP001758,Male,Yes,2,1,0.0,6250,1695.0,210.0,360.0,1,Semiurban,1.0
LP001760,Male,,,1,0.0,4758,0.0,158.0,480.0,1,Semiurban,1.0
LP001761,Male,No,0,1,1.0,6400,0.0,200.0,360.0,1,Rural,1.0
LP001765,Male,Yes,1,1,0.0,2491,2054.0,104.0,360.0,1,Semiurban,1.0
LP001768,Male,Yes,0,1,0.0,3716,0.0,42.0,180.0,1,Rural,1.0
LP001770,Male,No,0,0,0.0,3189,2598.0,120.0,,1,Rural,1.0
LP001776,Female,No,0,1,0.0,8333,0.0,280.0,360.0,1,Semiurban,1.0
LP001778,Male,Yes,1,1,0.0,3155,1779.0,140.0,360.0,1,Semiurban,1.0
LP001784,Male,Yes,1,1,0.0,5500,1260.0,170.0,360.0,1,Rural,1.0
LP001786,Male,Yes,0,1,0.0,5746,0.0,255.0,360.0,0,Urban,0.0
LP001788,Female,No,0,1,1.0,3463,0.0,122.0,360.0,0,Urban,1.0
LP001790,Female,No,1,1,0.0,3812,0.0,112.0,360.0,1,Rural,1.0
LP001792,Male,Yes,1,1,0.0,3315,0.0,96.0,360.0,1,Semiurban,1.0
LP001798,Male,Yes,2,1,0.0,5819,5000.0,120.0,360.0,1,Rural,1.0
LP001800,Male,Yes,1,0,0.0,2510,1983.0,140.0,180.0,1,Urban,0.0
LP001806,Male,No,0,1,0.0,2965,5701.0,155.0,60.0,1,Urban,1.0
LP001807,Male,Yes,2,1,1.0,6250,1300.0,108.0,360.0,1,Rural,1.0
LP001811,Male,Yes,0,0,0.0,3406,4417.0,123.0,360.0,1,Semiurban,1.0
LP001813,Male,No,0,1,1.0,6050,4333.0,120.0,180.0,1,Urban,0.0
LP001814,Male,Yes,2,1,0.0,9703,0.0,112.0,360.0,1,Urban,1.0
LP001819,Male,Yes,1,0,0.0,6608,0.0,137.0,180.0,1,Urban,1.0
LP001824,Male,Yes,1,1,0.0,2882,1843.0,123.0,480.0,1,Semiurban,1.0
LP001825,Male,Yes,0,1,0.0,1809,1868.0,90.0,360.0,1,Urban,1.0
LP001835,Male,Yes,0,0,0.0,1668,3890.0,201.0,360.0,0,Semiurban,0.0
LP001836,Female,No,2,1,0.0,3427,0.0,138.0,360.0,1,Urban,0.0
LP001841,Male,No,0,0,1.0,2583,2167.0,104.0,360.0,1,Rural,1.0
LP001843,Male,Yes,1,0,0.0,2661,7101.0,279.0,180.0,1,Semiurban,1.0
LP001844,Male,No,0,1,1.0,16250,0.0,192.0,360.0,0,Urban,0.0
LP001846,Female,No,3+,1,0.0,3083,0.0,255.0,360.0,1,Rural,1.0
LP001849,Male,No,0,0,0.0,6045,0.0,115.0,360.0,0,Rural,0.0
LP001854,Male,Yes,3+,1,0.0,5250,0.0,94.0,360.0,1,Urban,0.0
LP001859,Male,Yes,0,1,0.0,14683,2100.0,304.0,360.0,1,Rural,0.0
LP001864,Male,Yes,3+,0,0.0,4931,0.0,128.0,360.0,0,Semiurban,0.0
LP001865,Male,Yes,1,1,0.0,6083,4250.0,330.0,360.0,0,Urban,1.0
LP001868,Male,No,0,1,0.0,2060,2209.0,134.0,360.0,1,Semiurban,1.0
LP001870,Female,No,1,1,0.0,3481,0.0,155.0,36.0,1,Semiurban,0.0
LP001871,Female,No,0,1,0.0,7200,0.0,120.0,360.0,1,Rural,1.0
LP001872,Male,No,0,1,1.0,5166,0.0,128.0,360.0,1,Semiurban,1.0
LP001875,Male,No,0,1,0.0,4095,3447.0,151.0,360.0,1,Rural,1.0
LP001877,Male,Yes,2,1,0.0,4708,1387.0,150.0,360.0,1,Semiurban,1.0
LP001882,Male,Yes,3+,1,0.0,4333,1811.0,160.0,360.0,0,Urban,1.0
LP001883,Female,No,0,1,0.0,3418,0.0,135.0,360.0,1,Rural,0.0
LP001884,Female,No,1,1,0.0,2876,1560.0,90.0,360.0,1,Urban,1.0
LP001888,Female,No,0,1,0.0,3237,0.0,30.0,360.0,1,Urban,1.0
LP001891,Male,Yes,0,1,0.0,11146,0.0,136.0,360.0,1,Urban,1.0
LP001892,Male,No,0,1,0.0,2833,1857.0,126.0,360.0,1,Rural,1.0
LP001894,Male,Yes,0,1,0.0,2620,2223.0,150.0,360.0,1,Semiurban,1.0
LP001896,Male,Yes,2,1,0.0,3900,0.0,90.0,360.0,1,Semiurban,1.0
LP001900,Male,Yes,1,1,0.0,2750,1842.0,115.0,360.0,1,Semiurban,1.0
LP001903,Male,Yes,0,1,0.0,3993,3274.0,207.0,360.0,1,Semiurban,1.0
LP001904,Male,Yes,0,1,0.0,3103,1300.0,80.0,360.0,1,Urban,1.0
LP001907,Male,Yes,0,1,0.0,14583,0.0,436.0,360.0,1,Semiurban,1.0
LP001908,Female,Yes,0,0,0.0,4100,0.0,124.0,360.0,0,Rural,1.0
LP001910,Male,No,1,0,1.0,4053,2426.0,158.0,360.0,0,Urban,0.0
LP001914,Male,Yes,0,1,0.0,3927,800.0,112.0,360.0,1,Semiurban,1.0
LP001915,Male,Yes,2,1,0.0,2301,985.7999878,78.0,180.0,1,Urban,1.0
LP001917,Female,No,0,1,0.0,1811,1666.0,54.0,360.0,1,Urban,1.0
LP001922,Male,Yes,0,1,0.0,20667,0.0,0.0,360.0,1,Rural,0.0
LP001924,Male,No,0,1,0.0,3158,3053.0,89.0,360.0,1,Rural,1.0
LP001925,Female,No,0,1,1.0,2600,1717.0,99.0,300.0,1,Semiurban,0.0
LP001926,Male,Yes,0,1,0.0,3704,2000.0,120.0,360.0,1,Rural,1.0
LP001931,Female,No,0,1,0.0,4124,0.0,115.0,360.0,1,Semiurban,1.0
LP001935,Male,No,0,1,0.0,9508,0.0,187.0,360.0,1,Rural,1.0
LP001936,Male,Yes,0,1,0.0,3075,2416.0,139.0,360.0,1,Rural,1.0
LP001938,Male,Yes,2,1,0.0,4400,0.0,127.0,360.0,0,Semiurban,0.0
LP001940,Male,Yes,2,1,0.0,3153,1560.0,134.0,360.0,1,Urban,1.0
LP001945,Female,No,,1,0.0,5417,0.0,143.0,480.0,0,Urban,0.0
LP001947,Male,Yes,0,1,0.0,2383,3334.0,172.0,360.0,1,Semiurban,1.0
LP001949,Male,Yes,3+,1,0.0,4416,1250.0,110.0,360.0,1,Urban,1.0
LP001953,Male,Yes,1,1,0.0,6875,0.0,200.0,360.0,1,Semiurban,1.0
LP001954,Female,Yes,1,1,0.0,4666,0.0,135.0,360.0,1,Urban,1.0
LP001955,Female,No,0,1,0.0,5000,2541.0,151.0,480.0,1,Rural,0.0
LP001963,Male,Yes,1,1,0.0,2014,2925.0,113.0,360.0,1,Urban,0.0
LP001964,Male,Yes,0,0,0.0,1800,2934.0,93.0,360.0,0,Urban,0.0
LP001972,Male,Yes,,0,0.0,2875,1750.0,105.0,360.0,1,Semiurban,1.0
LP001974,Female,No,0,1,0.0,5000,0.0,132.0,360.0,1,Rural,1.0
LP001977,Male,Yes,1,1,0.0,1625,1803.0,96.0,360.0,1,Urban,1.0
LP001978,Male,No,0,1,0.0,4000,2500.0,140.0,360.0,1,Rural,1.0
LP001990,Male,No,0,0,0.0,2000,0.0,0.0,360.0,1,Urban,0.0
LP001993,Female,No,0,1,0.0,3762,1666.0,135.0,360.0,1,Rural,1.0
LP001994,Female,No,0,1,0.0,2400,1863.0,104.0,360.0,0,Urban,0.0
LP001996,Male,No,0,1,0.0,20233,0.0,480.0,360.0,1,Rural,0.0
LP001998,Male,Yes,2,0,0.0,7667,0.0,185.0,360.0,0,Rural,1.0
LP002002,Female,No,0,1,0.0,2917,0.0,84.0,360.0,1,Semiurban,1.0
LP002004,Male,No,0,0,0.0,2927,2405.0,111.0,360.0,1,Semiurban,1.0
LP002006,Female,No,0,1,0.0,2507,0.0,56.0,360.0,1,Rural,1.0
LP002008,Male,Yes,2,1,1.0,5746,0.0,144.0,84.0,0,Rural,1.0
LP002024,,Yes,0,1,0.0,2473,1843.0,159.0,360.0,1,Rural,0.0
LP002031,Male,Yes,1,0,0.0,3399,1640.0,111.0,180.0,1,Urban,1.0
LP002035,Male,Yes,2,1,0.0,3717,0.0,120.0,360.0,1,Semiurban,1.0
LP002036,Male,Yes,0,1,0.0,2058,2134.0,88.0,360.0,0,Urban,1.0
LP002043,Female,No,1,1,0.0,3541,0.0,112.0,360.0,0,Semiurban,1.0
LP002050,Male,Yes,1,1,1.0,10000,0.0,155.0,360.0,1,Rural,0.0
LP002051,Male,Yes,0,1,0.0,2400,2167.0,115.0,360.0,1,Semiurban,1.0
LP002053,Male,Yes,3+,1,0.0,4342,189.0,124.0,360.0,1,Semiurban,1.0
LP002054,Male,Yes,2,0,0.0,3601,1590.0,0.0,360.0,1,Rural,1.0
LP002055,Female,No,0,1,0.0,3166,2985.0,132.0,360.0,0,Rural,1.0
LP002065,Male,Yes,3+,1,0.0,15000,0.0,300.0,360.0,1,Rural,1.0
LP002067,Male,Yes,1,1,1.0,8666,4983.0,376.0,360.0,0,Rural,0.0
LP002068,Male,No,0,1,0.0,4917,0.0,130.0,360.0,0,Rural,1.0
LP002082,Male,Yes,0,1,1.0,5818,2160.0,184.0,360.0,1,Semiurban,1.0
LP002086,Female,Yes,0,1,0.0,4333,2451.0,110.0,360.0,1,Urban,0.0
LP002087,Female,No,0,1,0.0,2500,0.0,67.0,360.0,1,Urban,1.0
LP002097,Male,No,1,1,0.0,4384,1793.0,117.0,360.0,1,Urban,1.0
LP002098,Male,No,0,1,0.0,2935,0.0,98.0,360.0,1,Semiurban,1.0
LP002100,Male,No,,1,0.0,2833,0.0,71.0,360.0,1,Urban,1.0
LP002101,Male,Yes,0,1,0.0,63337,0.0,490.0,180.0,1,Urban,1.0
LP002103,,Yes,1,1,1.0,9833,1833.0,182.0,180.0,1,Urban,1.0
LP002106,Male,Yes,,1,1.0,5503,4490.0,70.0,,1,Semiurban,1.0
LP002110,Male,Yes,1,1,0.0,5250,688.0,160.0,360.0,1,Rural,1.0
LP002112,Male,Yes,2,1,1.0,2500,4600.0,176.0,360.0,1,Rural,1.0
LP002113,Female,No,3+,0,0.0,1830,0.0,0.0,360.0,0,Urban,0.0
LP002114,Female,No,0,1,0.0,4160,0.0,71.0,360.0,1,Semiurban,1.0
LP002115,Male,Yes,3+,0,0.0,2647,1587.0,173.0,360.0,1,Rural,0.0
LP002116,Female,No,0,1,0.0,2378,0.0,46.0,360.0,1,Rural,0.0
LP002119,Male,Yes,1,0,0.0,4554,1229.0,158.0,360.0,1,Urban,1.0
LP002126,Male,Yes,3+,0,0.0,3173,0.0,74.0,360.0,1,Semiurban,1.0
LP002128,Male,Yes,2,1,0.0,2583,2330.0,125.0,360.0,1,Rural,1.0
LP002129,Male,Yes,0,1,0.0,2499,2458.0,160.0,360.0,1,Semiurban,1.0
LP002130,Male,Yes,,0,0.0,3523,3230.0,152.0,360.0,0,Rural,0.0
LP002131,Male,Yes,2,0,0.0,3083,2168.0,126.0,360.0,1,Urban,1.0
LP002137,Male,Yes,0,1,0.0,6333,4583.0,259.0,360.0,0,Semiurban,1.0
LP002138,Male,Yes,0,1,0.0,2625,6250.0,187.0,360.0,1,Rural,1.0
LP002139,Male,Yes,0,1,0.0,9083,0.0,228.0,360.0,1,Semiurban,1.0
LP002140,Male,No,0,1,0.0,8750,4167.0,308.0,360.0,1,Rural,0.0
LP002141,Male,Yes,3+,1,0.0,2666,2083.0,95.0,360.0,1,Rural,1.0
LP002142,Female,Yes,0,1,1.0,5500,0.0,105.0,360.0,0,Rural,0.0
LP002143,Female,Yes,0,1,0.0,2423,505.0,130.0,360.0,1,Semiurban,1.0
LP002144,Female,No,,1,0.0,3813,0.0,116.0,180.0,1,Urban,1.0
LP002149,Male,Yes,2,1,0.0,8333,3167.0,165.0,360.0,1,Rural,1.0
LP002151,Male,Yes,1,1,0.0,3875,0.0,67.0,360.0,1,Urban,0.0
LP002158,Male,Yes,0,0,0.0,3000,1666.0,100.0,480.0,0,Urban,0.0
LP002160,Male,Yes,3+,1,0.0,5167,3167.0,200.0,360.0,1,Semiurban,1.0
LP002161,Female,No,1,1,0.0,4723,0.0,81.0,360.0,1,Semiurban,0.0
LP002170,Male,Yes,2,1,0.0,5000,3667.0,236.0,360.0,1,Semiurban,1.0
LP002175,Male,Yes,0,1,0.0,4750,2333.0,130.0,360.0,1,Urban,1.0
LP002178,Male,Yes,0,1,0.0,3013,3033.0,95.0,300.0,0,Urban,1.0
LP002180,Male,No,0,1,1.0,6822,0.0,141.0,360.0,1,Rural,1.0
LP002181,Male,No,0,0,0.0,6216,0.0,133.0,360.0,1,Rural,0.0
LP002187,Male,No,0,1,0.0,2500,0.0,96.0,480.0,1,Semiurban,0.0
LP002188,Male,No,0,1,0.0,5124,0.0,124.0,,0,Rural,0.0
LP002190,Male,Yes,1,1,0.0,6325,0.0,175.0,360.0,1,Semiurban,1.0
LP002191,Male,Yes,0,1,0.0,19730,5266.0,570.0,360.0,1,Rural,0.0
LP002194,Female,No,0,1,1.0,15759,0.0,55.0,360.0,1,Semiurban,1.0
LP002197,Male,Yes,2,1,0.0,5185,0.0,155.0,360.0,1,Semiurban,1.0
LP002201,Male,Yes,2,1,1.0,9323,7873.0,380.0,300.0,1,Rural,1.0
LP002205,Male,No,1,1,0.0,3062,1987.0,111.0,180.0,0,Urban,0.0
LP002209,Female,No,0,1,0.0,2764,1459.0,110.0,360.0,1,Urban,1.0
LP002211,Male,Yes,0,1,0.0,4817,923.0,120.0,180.0,1,Urban,1.0
LP002219,Male,Yes,3+,1,0.0,8750,4996.0,130.0,360.0,1,Rural,1.0
LP002223,Male,Yes,0,1,0.0,4310,0.0,130.0,360.0,0,Semiurban,1.0
LP002224,Male,No,0,1,0.0,3069,0.0,71.0,480.0,1,Urban,0.0
LP002225,Male,Yes,2,1,0.0,5391,0.0,130.0,360.0,1,Urban,1.0
LP002226,Male,Yes,0,1,0.0,3333,2500.0,128.0,360.0,1,Semiurban,1.0
LP002229,Male,No,0,1,0.0,5941,4232.0,296.0,360.0,1,Semiurban,1.0
LP002231,Female,No,0,1,0.0,6000,0.0,156.0,360.0,1,Urban,1.0
LP002234,Male,No,0,1,1.0,7167,0.0,128.0,360.0,1,Urban,1.0
LP002236,Male,Yes,2,1,0.0,4566,0.0,100.0,360.0,1,Urban,0.0
LP002237,Male,No,1,1,0.0,3667,0.0,113.0,180.0,1,Urban,1.0
LP002239,Male,No,0,0,0.0,2346,1600.0,132.0,360.0,1,Semiurban,1.0
LP002243,Male,Yes,0,0,0.0,3010,3136.0,0.0,360.0,0,Urban,0.0
LP002244,Male,Yes,0,1,0.0,2333,2417.0,136.0,360.0,1,Urban,1.0
LP002250,Male,Yes,0,1,0.0,5488,0.0,125.0,360.0,1,Rural,1.0
LP002255,Male,No,3+,1,0.0,9167,0.0,185.0,360.0,1,Rural,1.0
LP002262,Male,Yes,3+,1,0.0,9504,0.0,275.0,360.0,1,Rural,1.0
LP002263,Male,Yes,0,1,0.0,2583,2115.0,120.0,360.0,0,Urban,1.0
LP002265,Male,Yes,2,0,0.0,1993,1625.0,113.0,180.0,1,Semiurban,1.0
LP002266,Male,Yes,2,1,0.0,3100,1400.0,113.0,360.0,1,Urban,1.0
LP002272,Male,Yes,2,1,0.0,3276,484.0,135.0,360.0,0,Semiurban,1.0
LP002277,Female,No,0,1,0.0,3180,0.0,71.0,360.0,0,Urban,0.0
LP002281,Male,Yes,0,1,0.0,3033,1459.0,95.0,360.0,1,Urban,1.0
LP002284,Male,No,0,0,0.0,3902,1666.0,109.0,360.0,1,Rural,1.0
LP002287,Female,No,0,1,0.0,1500,1800.0,103.0,360.0,0,Semiurban,0.0
LP002288,Male,Yes,2,0,0.0,2889,0.0,45.0,180.0,0,Urban,0.0
LP002296,Male,No,0,0,0.0,2755,0.0,65.0,300.0,1,Rural,0.0
LP002297,Male,No,0,1,0.0,2500,20000.0,103.0,360.0,1,Semiurban,1.0
LP002300,Female,No,0,0,0.0,1963,0.0,53.0,360.0,1,Semiurban,1.0
LP002301,Female,No,0,1,1.0,7441,0.0,194.0,360.0,1,Rural,0.0
LP002305,Female,No,0,1,0.0,4547,0.0,115.0,360.0,1,Semiurban,1.0
LP002308,Male,Yes,0,0,0.0,2167,2400.0,115.0,360.0,1,Urban,1.0
LP002314,Female,No,0,0,0.0,2213,0.0,66.0,360.0,1,Rural,1.0
LP002315,Male,Yes,1,1,0.0,8300,0.0,152.0,300.0,0,Semiurban,0.0
LP002317,Male,Yes,3+,1,0.0,81000,0.0,360.0,360.0,0,Rural,0.0
LP002318,Female,No,1,0,1.0,3867,0.0,62.0,360.0,1,Semiurban,0.0
LP002319,Male,Yes,0,1,0.0,6256,0.0,160.0,360.0,0,Urban,1.0
LP002328,Male,Yes,0,0,0.0,6096,0.0,218.0,360.0,0,Rural,0.0
LP002332,Male,Yes,0,0,0.0,2253,2033.0,110.0,360.0,1,Rural,1.0
LP002335,Female,Yes,0,0,0.0,2149,3237.0,178.0,360.0,0,Semiurban,0.0
LP002337,Female,No,0,1,0.0,2995,0.0,60.0,360.0,1,Urban,1.0
LP002341,Female,No,1,1,0.0,2600,0.0,160.0,360.0,1,Urban,0.0
LP002342,Male,Yes,2,1,1.0,1600,20000.0,239.0,360.0,1,Urban,0.0
LP002345,Male,Yes,0,1,0.0,1025,2773.0,112.0,360.0,1,Rural,1.0
LP002347,Male,Yes,0,1,0.0,3246,1417.0,138.0,360.0,1,Semiurban,1.0
LP002348,Male,Yes,0,1,0.0,5829,0.0,138.0,360.0,1,Rural,1.0
LP002357,Female,No,0,0,0.0,2720,0.0,80.0,,0,Urban,0.0
LP002361,Male,Yes,0,1,0.0,1820,1719.0,100.0,360.0,1,Urban,1.0
LP002362,Male,Yes,1,1,0.0,7250,1667.0,110.0,,0,Urban,0.0
LP002364,Male,Yes,0,1,0.0,14880,0.0,96.0,360.0,1,Semiurban,1.0
LP002366,Male,Yes,0,1,0.0,2666,4300.0,121.0,360.0,1,Rural,1.0
LP002367,Female,No,1,0,0.0,4606,0.0,81.0,360.0,1,Rural,0.0
LP002368,Male,Yes,2,1,0.0,5935,0.0,133.0,360.0,1,Semiurban,1.0
LP002369,Male,Yes,0,1,0.0,2920,16.12000084,87.0,360.0,1,Rural,1.0
LP002370,Male,No,0,0,0.0,2717,0.0,60.0,180.0,1,Urban,1.0
LP002377,Female,No,1,1,1.0,8624,0.0,150.0,360.0,1,Semiurban,1.0
LP002379,Male,No,0,1,0.0,6500,0.0,105.0,360.0,0,Rural,0.0
LP002386,Male,No,0,1,0.0,12876,0.0,405.0,360.0,1,Semiurban,1.0
LP002387,Male,Yes,0,1,0.0,2425,2340.0,143.0,360.0,1,Semiurban,1.0
LP002390,Male,No,0,1,0.0,3750,0.0,100.0,360.0,1,Urban,1.0
LP002393,Female,,,1,0.0,10047,0.0,0.0,240.0,1,Semiurban,1.0
LP002398,Male,No,0,1,0.0,1926,1851.0,50.0,360.0,1,Semiurban,1.0
LP002401,Male,Yes,0,1,0.0,2213,1125.0,0.0,360.0,1,Urban,1.0
LP002403,Male,No,0,1,1.0,10416,0.0,187.0,360.0,0,Urban,0.0
LP002407,Female,Yes,0,0,1.0,7142,0.0,138.0,360.0,1,Rural,1.0
LP002408,Male,No,0,1,0.0,3660,5064.0,187.0,360.0,1,Semiurban,1.0
LP002409,Male,Yes,0,1,0.0,7901,1833.0,180.0,360.0,1,Rural,1.0
LP002418,Male,No,3+,0,0.0,4707,1993.0,148.0,360.0,1,Semiurban,1.0
LP002422,Male,No,1,1,0.0,37719,0.0,152.0,360.0,1,Semiurban,1.0
LP002424,Male,Yes,0,1,0.0,7333,8333.0,175.0,300.0,0,Rural,1.0
LP002429,Male,Yes,1,1,1.0,3466,1210.0,130.0,360.0,1,Rural,1.0
LP002434,Male,Yes,2,0,0.0,4652,0.0,110.0,360.0,1,Rural,1.0
LP002435,Male,Yes,0,1,0.0,3539,1376.0,55.0,360.0,1,Rural,0.0
LP002443,Male,Yes,2,1,0.0,3340,1710.0,150.0,360.0,0,Rural,0.0
LP002444,Male,No,1,0,1.0,2769,1542.0,190.0,360.0,0,Semiurban,0.0
LP002446,Male,Yes,2,0,0.0,2309,1255.0,125.0,360.0,0,Rural,0.0
LP002447,Male,Yes,2,0,0.0,1958,1456.0,60.0,300.0,0,Urban,1.0
LP002448,Male,Yes,0,1,0.0,3948,1733.0,149.0,360.0,0,Rural,0.0
LP002449,Male,Yes,0,1,0.0,2483,2466.0,90.0,180.0,0,Rural,1.0
LP002453,Male,No,0,1,1.0,7085,0.0,84.0,360.0,1,Semiurban,1.0
LP002455,Male,Yes,2,1,0.0,3859,0.0,96.0,360.0,1,Semiurban,1.0
LP002459,Male,Yes,0,1,0.0,4301,0.0,118.0,360.0,1,Urban,1.0
LP002467,Male,Yes,0,1,0.0,3708,2569.0,173.0,360.0,1,Urban,0.0
LP002472,Male,No,2,1,0.0,4354,0.0,136.0,360.0,1,Rural,1.0
LP002473,Male,Yes,0,1,0.0,8334,0.0,160.0,360.0,1,Semiurban,0.0
LP002478,,Yes,0,1,1.0,2083,4083.0,160.0,360.0,0,Semiurban,1.0
LP002484,Male,Yes,3+,1,0.0,7740,0.0,128.0,180.0,1,Urban,1.0
LP002487,Male,Yes,0,1,0.0,3015,2188.0,153.0,360.0,1,Rural,1.0
LP002489,Female,No,1,0,0.0,5191,0.0,132.0,360.0,1,Semiurban,1.0
LP002493,Male,No,0,1,0.0,4166,0.0,98.0,360.0,0,Semiurban,0.0
LP002494,Male,No,0,1,0.0,6000,0.0,140.0,360.0,1,Rural,1.0
LP002500,Male,Yes,3+,0,0.0,2947,1664.0,70.0,180.0,0,Urban,0.0
LP002501,,Yes,0,1,0.0,16692,0.0,110.0,360.0,1,Semiurban,1.0
LP002502,Female,Yes,2,0,0.0,210,2917.0,98.0,360.0,1,Semiurban,1.0
LP002505,Male,Yes,0,1,0.0,4333,2451.0,110.0,360.0,1,Urban,0.0
LP002515,Male,Yes,1,1,1.0,3450,2079.0,162.0,360.0,1,Semiurban,1.0
LP002517,Male,Yes,1,0,0.0,2653,1500.0,113.0,180.0,0,Rural,0.0
LP002519,Male,Yes,3+,1,0.0,4691,0.0,100.0,360.0,1,Semiurban,1.0
LP002522,Female,No,0,1,1.0,2500,0.0,93.0,360.0,0,Urban,1.0
LP002524,Male,No,2,1,0.0,5532,4648.0,162.0,360.0,1,Rural,1.0
LP002527,Male,Yes,2,1,1.0,16525,1014.0,150.0,360.0,1,Rural,1.0
LP002529,Male,Yes,2,1,0.0,6700,1750.0,230.0,300.0,1,Semiurban,1.0
LP002530,,Yes,2,1,0.0,2873,1872.0,132.0,360.0,0,Semiurban,0.0
LP002531,Male,Yes,1,1,1.0,16667,2250.0,86.0,360.0,1,Semiurban,1.0
LP002533,Male,Yes,2,1,0.0,2947,1603.0,0.0,360.0,1,Urban,0.0
LP002534,Female,No,0,0,0.0,4350,0.0,154.0,360.0,1,Rural,1.0
LP002536,Male,Yes,3+,0,0.0,3095,0.0,113.0,360.0,1,Rural,1.0
LP002537,Male,Yes,0,1,0.0,2083,3150.0,128.0,360.0,1,Semiurban,1.0
LP002541,Male,Yes,0,1,0.0,10833,0.0,234.0,360.0,1,Semiurban,1.0
LP002543,Male,Yes,2,1,0.0,8333,0.0,246.0,360.0,1,Semiurban,1.0
LP002544,Male,Yes,1,0,0.0,1958,2436.0,131.0,360.0,1,Rural,1.0
LP002545,Male,No,2,1,0.0,3547,0.0,80.0,360.0,0,Rural,0.0
LP002547,Male,Yes,1,1,0.0,18333,0.0,500.0,360.0,1,Urban,0.0
LP002555,Male,Yes,2,1,1.0,4583,2083.0,160.0,360.0,1,Semiurban,1.0
LP002556,Male,No,0,1,0.0,2435,0.0,75.0,360.0,1,Urban,0.0
LP002560,Male,No,0,0,0.0,2699,2785.0,96.0,360.0,0,Semiurban,1.0
LP002562,Male,Yes,1,0,0.0,5333,1131.0,186.0,360.0,0,Urban,1.0
LP002571,Male,No,0,0,0.0,3691,0.0,110.0,360.0,1,Rural,1.0
LP002582,Female,No,0,0,1.0,17263,0.0,225.0,360.0,1,Semiurban,1.0
LP002585,Male,Yes,0,1,0.0,3597,2157.0,119.0,360.0,0,Rural,0.0
LP002586,Female,Yes,1,1,0.0,3326,913.0,105.0,84.0,1,Semiurban,1.0
LP002587,Male,Yes,0,0,0.0,2600,1700.0,107.0,360.0,1,Rural,1.0
LP002588,Male,Yes,0,1,0.0,4625,2857.0,111.0,12.0,0,Urban,1.0
LP002600,Male,Yes,1,1,1.0,2895,0.0,95.0,360.0,1,Semiurban,1.0
LP002602,Male,No,0,1,0.0,6283,4416.0,209.0,360.0,0,Rural,0.0
LP002603,Female,No,0,1,0.0,645,3683.0,113.0,480.0,1,Rural,1.0
LP002606,Female,No,0,1,0.0,3159,0.0,100.0,360.0,1,Semiurban,1.0
LP002615,Male,Yes,2,1,0.0,4865,5624.0,208.0,360.0,1,Semiurban,1.0
LP002618,Male,Yes,1,0,0.0,4050,5302.0,138.0,360.0,0,Rural,0.0
LP002619,Male,Yes,0,0,0.0,3814,1483.0,124.0,300.0,1,Semiurban,1.0
LP002622,Male,Yes,2,1,0.0,3510,4416.0,243.0,360.0,1,Rural,1.0
LP002624,Male,Yes,0,1,0.0,20833,6667.0,480.0,360.0,0,Urban,1.0
LP002625,,No,0,1,0.0,3583,0.0,96.0,360.0,1,Urban,0.0
LP002626,Male,Yes,0,1,1.0,2479,3013.0,188.0,360.0,1,Urban,1.0
LP002634,Female,No,1,1,0.0,13262,0.0,40.0,360.0,1,Urban,1.0
LP002637,Male,No,0,0,0.0,3598,1287.0,100.0,360.0,1,Rural,0.0
LP002640,Male,Yes,1,1,0.0,6065,2004.0,250.0,360.0,1,Semiurban,1.0
LP002643,Male,Yes,2,1,0.0,3283,2035.0,148.0,360.0,1,Urban,1.0
LP002648,Male,Yes,0,1,0.0,2130,6666.0,70.0,180.0,1,Semiurban,0.0
LP002652,Male,No,0,1,0.0,5815,3666.0,311.0,360.0,1,Rural,0.0
LP002659,Male,Yes,3+,1,0.0,3466,3428.0,150.0,360.0,1,Rural,1.0
LP002670,Female,Yes,2,1,0.0,2031,1632.0,113.0,480.0,1,Semiurban,1.0
LP002682,Male,Yes,,0,0.0,3074,1800.0,123.0,360.0,0,Semiurban,0.0
LP002683,Male,No,0,1,0.0,4683,1915.0,185.0,360.0,1,Semiurban,0.0
LP002684,Female,No,0,0,0.0,3400,0.0,95.0,360.0,1,Rural,0.0
LP002689,Male,Yes,2,0,0.0,2192,1742.0,45.0,360.0,1,Semiurban,1.0
LP002690,Male,No,0,1,0.0,2500,0.0,55.0,360.0,1,Semiurban,1.0
LP002692,Male,Yes,3+,1,1.0,5677,1424.0,100.0,360.0,1,Rural,1.0
LP002693,Male,Yes,2,1,1.0,7948,7166.0,480.0,360.0,1,Rural,1.0
LP002697,Male,No,0,1,0.0,4680,2087.0,0.0,360.0,1,Semiurban,0.0
LP002699,Male,Yes,2,1,1.0,17500,0.0,400.0,360.0,1,Rural,1.0
LP002705,Male,Yes,0,1,0.0,3775,0.0,110.0,360.0,1,Semiurban,1.0
LP002706,Male,Yes,1,0,0.0,5285,1430.0,161.0,360.0,0,Semiurban,1.0
LP002714,Male,No,1,0,0.0,2679,1302.0,94.0,360.0,1,Semiurban,1.0
LP002716,Male,No,0,0,0.0,6783,0.0,130.0,360.0,1,Semiurban,1.0
LP002717,Male,Yes,0,1,0.0,1025,5500.0,216.0,360.0,0,Rural,1.0
LP002720,Male,Yes,3+,1,0.0,4281,0.0,100.0,360.0,1,Urban,1.0
LP002723,Male,No,2,1,0.0,3588,0.0,110.0,360.0,0,Rural,0.0
LP002729,Male,No,1,1,0.0,11250,0.0,196.0,360.0,0,Semiurban,0.0
LP002731,Female,No,0,0,1.0,18165,0.0,125.0,360.0,1,Urban,1.0
LP002732,Male,No,0,0,0.0,2550,2042.0,126.0,360.0,1,Rural,1.0
LP002734,Male,Yes,0,1,0.0,6133,3906.0,324.0,360.0,1,Urban,1.0
LP002738,Male,No,2,1,0.0,3617,0.0,107.0,360.0,1,Semiurban,1.0
LP002739,Male,Yes,0,0,0.0,2917,536.0,66.0,360.0,1,Rural,0.0
LP002740,Male,Yes,3+,1,0.0,6417,0.0,157.0,180.0,1,Rural,1.0
LP002741,Female,Yes,1,1,0.0,4608,2845.0,140.0,180.0,1,Semiurban,1.0
LP002743,Female,No,0,1,0.0,2138,0.0,99.0,360.0,0,Semiurban,0.0
LP002753,Female,No,1,1,0.0,3652,0.0,95.0,360.0,1,Semiurban,1.0
LP002755,Male,Yes,1,0,0.0,2239,2524.0,128.0,360.0,1,Urban,1.0
LP002757,Female,Yes,0,0,0.0,3017,663.0,102.0,360.0,0,Semiurban,1.0
LP002767,Male,Yes,0,1,0.0,2768,1950.0,155.0,360.0,1,Rural,1.0
LP002768,Male,No,0,0,0.0,3358,0.0,80.0,36.0,1,Semiurban,0.0
LP002772,Male,No,0,1,0.0,2526,1783.0,145.0,360.0,1,Rural,1.0
LP002776,Female,No,0,1,0.0,5000,0.0,103.0,360.0,0,Semiurban,0.0
LP002777,Male,Yes,0,1,0.0,2785,2016.0,110.0,360.0,1,Rural,1.0
LP002778,Male,Yes,2,1,1.0,6633,0.0,0.0,360.0,0,Rural,0.0
LP002784,Male,Yes,1,0,0.0,2492,2375.0,0.0,360.0,1,Rural,1.0
LP002785,Male,Yes,1,1,0.0,3333,3250.0,158.0,360.0,1,Urban,1.0
LP002788,Male,Yes,0,0,0.0,2454,2333.0,181.0,360.0,0,Urban,0.0
LP002789,Male,Yes,0,1,0.0,3593,4266.0,132.0,180.0,0,Rural,0.0
LP002792,Male,Yes,1,1,0.0,5468,1032.0,26.0,360.0,1,Semiurban,1.0
LP002794,Female,No,0,1,0.0,2667,1625.0,84.0,360.0,0,Urban,1.0
LP002795,Male,Yes,3+,1,1.0,10139,0.0,260.0,360.0,1,Semiurban,1.0
LP002798,Male,Yes,0,1,0.0,3887,2669.0,162.0,360.0,1,Semiurban,1.0
LP002804,Female,Yes,0,1,0.0,4180,2306.0,182.0,360.0,1,Semiurban,1.0
LP002807,Male,Yes,2,0,0.0,3675,242.0,108.0,360.0,1,Semiurban,1.0
LP002813,Female,Yes,1,1,1.0,19484,0.0,600.0,360.0,1,Semiurban,1.0
LP002820,Male,Yes,0,1,0.0,5923,2054.0,211.0,360.0,1,Rural,1.0
LP002821,Male,No,0,0,1.0,5800,0.0,132.0,360.0,1,Semiurban,1.0
LP002832,Male,Yes,2,1,0.0,8799,0.0,258.0,360.0,0,Urban,0.0
LP002833,Male,Yes,0,0,0.0,4467,0.0,120.0,360.0,0,Rural,1.0
LP002836,Male,No,0,1,0.0,3333,0.0,70.0,360.0,1,Urban,1.0
LP002837,Male,Yes,3+,1,0.0,3400,2500.0,123.0,360.0,0,Rural,0.0
LP002840,Female,No,0,1,0.0,2378,0.0,9.0,360.0,1,Urban,0.0
LP002841,Male,Yes,0,1,0.0,3166,2064.0,104.0,360.0,0,Urban,0.0
LP002842,Male,Yes,1,1,0.0,3417,1750.0,186.0,360.0,1,Urban,1.0
LP002847,Male,Yes,,1,0.0,5116,1451.0,165.0,360.0,0,Urban,0.0
LP002855,Male,Yes,2,1,0.0,16666,0.0,275.0,360.0,1,Urban,1.0
LP002862,Male,Yes,2,0,0.0,6125,1625.0,187.0,480.0,1,Semiurban,0.0
LP002863,Male,Yes,3+,1,0.0,6406,0.0,150.0,360.0,1,Semiurban,0.0
LP002868,Male,Yes,2,1,0.0,3159,461.0,108.0,84.0,1,Urban,1.0
LP002872,,Yes,0,1,0.0,3087,2210.0,136.0,360.0,0,Semiurban,0.0
LP002874,Male,No,0,1,0.0,3229,2739.0,110.0,360.0,1,Urban,1.0
LP002877,Male,Yes,1,1,0.0,1782,2232.0,107.0,360.0,1,Rural,1.0
LP002888,Male,No,0,1,0.0,3182,2917.0,161.0,360.0,1,Urban,1.0
LP002892,Male,Yes,2,1,0.0,6540,0.0,205.0,360.0,1,Semiurban,1.0
LP002893,Male,No,0,1,0.0,1836,33837.0,90.0,360.0,1,Urban,0.0
LP002894,Female,Yes,0,1,0.0,3166,0.0,36.0,360.0,1,Semiurban,1.0
LP002898,Male,Yes,1,1,0.0,1880,0.0,61.0,360.0,0,Rural,0.0
LP002911,Male,Yes,1,1,0.0,2787,1917.0,146.0,360.0,0,Rural,0.0
LP002912,Male,Yes,1,1,0.0,4283,3000.0,172.0,84.0,1,Rural,0.0
LP002916,Male,Yes,0,1,0.0,2297,1522.0,104.0,360.0,1,Urban,1.0
LP002917,Female,No,0,0,0.0,2165,0.0,70.0,360.0,1,Semiurban,1.0
LP002925,,No,0,1,0.0,4750,0.0,94.0,360.0,1,Semiurban,1.0
LP002926,Male,Yes,2,1,1.0,2726,0.0,106.0,360.0,0,Semiurban,0.0
LP002928,Male,Yes,0,1,0.0,3000,3416.0,56.0,180.0,1,Semiurban,1.0
LP002931,Male,Yes,2,1,1.0,6000,0.0,205.0,240.0,1,Semiurban,0.0
LP002933,,No,3+,1,1.0,9357,0.0,292.0,360.0,1,Semiurban,1.0
LP002936,Male,Yes,0,1,0.0,3859,3300.0,142.0,180.0,1,Rural,1.0
LP002938,Male,Yes,0,1,1.0,16120,0.0,260.0,360.0,1,Urban,1.0
LP002940,Male,No,0,0,0.0,3833,0.0,110.0,360.0,1,Rural,1.0
LP002941,Male,Yes,2,0,1.0,6383,1000.0,187.0,360.0,1,Rural,0.0
LP002943,Male,No,,1,0.0,2987,0.0,88.0,360.0,0,Semiurban,0.0
LP002945,Male,Yes,0,1,1.0,9963,0.0,180.0,360.0,1,Rural,1.0
LP002948,Male,Yes,2,1,0.0,5780,0.0,192.0,360.0,1,Urban,1.0
LP002949,Female,No,3+,1,0.0,416,41667.0,350.0,180.0,0,Urban,0.0
LP002950,Male,Yes,0,0,0.0,2894,2792.0,155.0,360.0,1,Rural,1.0
LP002953,Male,Yes,3+,1,0.0,5703,0.0,128.0,360.0,1,Urban,1.0
LP002958,Male,No,0,1,0.0,3676,4301.0,172.0,360.0,1,Rural,1.0
LP002959,Female,Yes,1,1,0.0,12000,0.0,496.0,360.0,1,Semiurban,1.0
LP002960,Male,Yes,0,0,0.0,2400,3800.0,0.0,180.0,1,Urban,0.0
LP002961,Male,Yes,1,1,0.0,3400,2500.0,173.0,360.0,1,Semiurban,1.0
LP002964,Male,Yes,2,0,0.0,3987,1411.0,157.0,360.0,1,Rural,1.0
LP002974,Male,Yes,0,1,0.0,3232,1950.0,108.0,360.0,1,Rural,1.0
LP002978,Female,No,0,1,0.0,2900,0.0,71.0,360.0,1,Rural,1.0
LP002979,Male,Yes,3+,1,0.0,4106,0.0,40.0,180.0,1,Rural,1.0
LP002983,Male,Yes,1,1,0.0,8072,240.0,253.0,360.0,1,Urban,1.0
LP002984,Male,Yes,2,1,0.0,7583,0.0,187.0,360.0,1,Urban,1.0
LP002990,Female,No,0,1,1.0,4583,0.0,133.0,360.0,0,Semiurban,0.0
1 Loan_ID Gender Married Dependents Education Self_Employed ApplicantIncome CoapplicantIncome LoanAmount Loan_Amount_Term Credit_History Property_Area Loan_Status
2 LP001002 Male No 0 1 0.0 5849 0.0 360.0 1.0 0 Y 0.0
3 LP001003 Male Yes 1 1 0.0 4583 1508.0 128.0 360.0 1 Rural 0.0
4 LP001005 Male Yes 0 1 1.0 3000 0.0 66.0 360.0 1 Urban 1.0
5 LP001006 Male Yes 0 0 0.0 2583 2358.0 120.0 360.0 1 Urban 1.0
6 LP001008 Male No 0 1 0.0 6000 0.0 141.0 360.0 1 Urban 1.0
7 LP001011 Male Yes 2 1 1.0 5417 4196.0 267.0 360.0 1 Urban 1.0
8 LP001013 Male Yes 0 0 0.0 2333 1516.0 95.0 360.0 1 Urban 1.0
9 LP001014 Male Yes 3+ 1 0.0 3036 2504.0 158.0 360.0 0 Semiurban 0.0
10 LP001018 Male Yes 2 1 0.0 4006 1526.0 168.0 360.0 1 Urban 1.0
11 LP001020 Male Yes 1 1 0.0 12841 10968.0 349.0 360.0 1 Semiurban 0.0
12 LP001024 Male Yes 2 1 0.0 3200 700.0 70.0 360.0 1 Urban 1.0
13 LP001027 Male Yes 2 1 0.0 2500 1840.0 109.0 360.0 1 Urban 1.0
14 LP001028 Male Yes 2 1 0.0 3073 8106.0 200.0 360.0 1 Urban 1.0
15 LP001029 Male No 0 1 0.0 1853 2840.0 114.0 360.0 1 Rural 0.0
16 LP001030 Male Yes 2 1 0.0 1299 1086.0 17.0 120.0 1 Urban 1.0
17 LP001032 Male No 0 1 0.0 4950 0.0 125.0 360.0 1 Urban 1.0
18 LP001034 Male No 1 0 0.0 3596 0.0 100.0 240.0 0 Urban 1.0
19 LP001036 Female No 0 1 0.0 3510 0.0 76.0 360.0 0 Urban 0.0
20 LP001038 Male Yes 0 0 0.0 4887 0.0 133.0 360.0 1 Rural 0.0
21 LP001041 Male Yes 0 1 0.0 2600 3500.0 115.0 1 Urban 1.0
22 LP001043 Male Yes 0 0 0.0 7660 0.0 104.0 360.0 0 Urban 0.0
23 LP001046 Male Yes 1 1 0.0 5955 5625.0 315.0 360.0 1 Urban 1.0
24 LP001047 Male Yes 0 0 0.0 2600 1911.0 116.0 360.0 0 Semiurban 0.0
25 LP001050 Yes 2 0 0.0 3365 1917.0 112.0 360.0 0 Rural 0.0
26 LP001052 Male Yes 1 1 0.0 3717 2925.0 151.0 360.0 0 Semiurban 0.0
27 LP001066 Male Yes 0 1 1.0 9560 0.0 191.0 360.0 1 Semiurban 1.0
28 LP001068 Male Yes 0 1 0.0 2799 2253.0 122.0 360.0 1 Semiurban 1.0
29 LP001073 Male Yes 2 0 0.0 4226 1040.0 110.0 360.0 1 Urban 1.0
30 LP001086 Male No 0 0 0.0 1442 0.0 35.0 360.0 1 Urban 0.0
31 LP001087 Female No 2 1 0.0 3750 2083.0 120.0 360.0 1 Semiurban 1.0
32 LP001091 Male Yes 1 1 0.0 4166 3369.0 201.0 360.0 0 Urban 0.0
33 LP001095 Male No 0 1 0.0 3167 0.0 74.0 360.0 1 Urban 0.0
34 LP001097 Male No 1 1 1.0 4692 0.0 106.0 360.0 1 Rural 0.0
35 LP001098 Male Yes 0 1 0.0 3500 1667.0 114.0 360.0 1 Semiurban 1.0
36 LP001100 Male No 3+ 1 0.0 12500 3000.0 320.0 360.0 1 Rural 0.0
37 LP001106 Male Yes 0 1 0.0 2275 2067.0 0.0 360.0 1 Urban 1.0
38 LP001109 Male Yes 0 1 0.0 1828 1330.0 100.0 0 Urban 0.0
39 LP001112 Female Yes 0 1 0.0 3667 1459.0 144.0 360.0 1 Semiurban 1.0
40 LP001114 Male No 0 1 0.0 4166 7210.0 184.0 360.0 1 Urban 1.0
41 LP001116 Male No 0 0 0.0 3748 1668.0 110.0 360.0 1 Semiurban 1.0
42 LP001119 Male No 0 1 0.0 3600 0.0 80.0 360.0 1 Urban 0.0
43 LP001120 Male No 0 1 0.0 1800 1213.0 47.0 360.0 1 Urban 1.0
44 LP001123 Male Yes 0 1 0.0 2400 0.0 75.0 360.0 0 Urban 1.0
45 LP001131 Male Yes 0 1 0.0 3941 2336.0 134.0 360.0 1 Semiurban 1.0
46 LP001136 Male Yes 0 0 1.0 4695 0.0 96.0 1 Urban 1.0
47 LP001137 Female No 0 1 0.0 3410 0.0 88.0 1 Urban 1.0
48 LP001138 Male Yes 1 1 0.0 5649 0.0 44.0 360.0 1 Urban 1.0
49 LP001144 Male Yes 0 1 0.0 5821 0.0 144.0 360.0 1 Urban 1.0
50 LP001146 Female Yes 0 1 0.0 2645 3440.0 120.0 360.0 0 Urban 0.0
51 LP001151 Female No 0 1 0.0 4000 2275.0 144.0 360.0 1 Semiurban 1.0
52 LP001155 Female Yes 0 0 0.0 1928 1644.0 100.0 360.0 1 Semiurban 1.0
53 LP001157 Female No 0 1 0.0 3086 0.0 120.0 360.0 1 Semiurban 1.0
54 LP001164 Female No 0 1 0.0 4230 0.0 112.0 360.0 1 Semiurban 0.0
55 LP001179 Male Yes 2 1 0.0 4616 0.0 134.0 360.0 1 Urban 0.0
56 LP001186 Female Yes 1 1 1.0 11500 0.0 286.0 360.0 0 Urban 0.0
57 LP001194 Male Yes 2 1 0.0 2708 1167.0 97.0 360.0 1 Semiurban 1.0
58 LP001195 Male Yes 0 1 0.0 2132 1591.0 96.0 360.0 1 Semiurban 1.0
59 LP001197 Male Yes 0 1 0.0 3366 2200.0 135.0 360.0 1 Rural 0.0
60 LP001198 Male Yes 1 1 0.0 8080 2250.0 180.0 360.0 1 Urban 1.0
61 LP001199 Male Yes 2 0 0.0 3357 2859.0 144.0 360.0 1 Urban 1.0
62 LP001205 Male Yes 0 1 0.0 2500 3796.0 120.0 360.0 1 Urban 1.0
63 LP001206 Male Yes 3+ 1 0.0 3029 0.0 99.0 360.0 1 Urban 1.0
64 LP001207 Male Yes 0 0 1.0 2609 3449.0 165.0 180.0 0 Rural 0.0
65 LP001213 Male Yes 1 1 0.0 4945 0.0 0.0 360.0 0 Rural 0.0
66 LP001222 Female No 0 1 0.0 4166 0.0 116.0 360.0 0 Semiurban 0.0
67 LP001225 Male Yes 0 1 0.0 5726 4595.0 258.0 360.0 1 Semiurban 0.0
68 LP001228 Male No 0 0 0.0 3200 2254.0 126.0 180.0 0 Urban 0.0
69 LP001233 Male Yes 1 1 0.0 10750 0.0 312.0 360.0 1 Urban 1.0
70 LP001238 Male Yes 3+ 0 1.0 7100 0.0 125.0 60.0 1 Urban 1.0
71 LP001241 Female No 0 1 0.0 4300 0.0 136.0 360.0 0 Semiurban 0.0
72 LP001243 Male Yes 0 1 0.0 3208 3066.0 172.0 360.0 1 Urban 1.0
73 LP001245 Male Yes 2 0 1.0 1875 1875.0 97.0 360.0 1 Semiurban 1.0
74 LP001248 Male No 0 1 0.0 3500 0.0 81.0 300.0 1 Semiurban 1.0
75 LP001250 Male Yes 3+ 0 0.0 4755 0.0 95.0 0 Semiurban 0.0
76 LP001253 Male Yes 3+ 1 1.0 5266 1774.0 187.0 360.0 1 Semiurban 1.0
77 LP001255 Male No 0 1 0.0 3750 0.0 113.0 480.0 1 Urban 0.0
78 LP001256 Male No 0 1 0.0 3750 4750.0 176.0 360.0 1 Urban 0.0
79 LP001259 Male Yes 1 1 1.0 1000 3022.0 110.0 360.0 1 Urban 0.0
80 LP001263 Male Yes 3+ 1 0.0 3167 4000.0 180.0 300.0 0 Semiurban 0.0
81 LP001264 Male Yes 3+ 0 1.0 3333 2166.0 130.0 360.0 0 Semiurban 1.0
82 LP001265 Female No 0 1 0.0 3846 0.0 111.0 360.0 1 Semiurban 1.0
83 LP001266 Male Yes 1 1 1.0 2395 0.0 0.0 360.0 1 Semiurban 1.0
84 LP001267 Female Yes 2 1 0.0 1378 1881.0 167.0 360.0 1 Urban 0.0
85 LP001273 Male Yes 0 1 0.0 6000 2250.0 265.0 360.0 0 Semiurban 0.0
86 LP001275 Male Yes 1 1 0.0 3988 0.0 50.0 240.0 1 Urban 1.0
87 LP001279 Male No 0 1 0.0 2366 2531.0 136.0 360.0 1 Semiurban 1.0
88 LP001280 Male Yes 2 0 0.0 3333 2000.0 99.0 360.0 0 Semiurban 1.0
89 LP001282 Male Yes 0 1 0.0 2500 2118.0 104.0 360.0 1 Semiurban 1.0
90 LP001289 Male No 0 1 0.0 8566 0.0 210.0 360.0 1 Urban 1.0
91 LP001310 Male Yes 0 1 0.0 5695 4167.0 175.0 360.0 1 Semiurban 1.0
92 LP001316 Male Yes 0 1 0.0 2958 2900.0 131.0 360.0 1 Semiurban 1.0
93 LP001318 Male Yes 2 1 0.0 6250 5654.0 188.0 180.0 1 Semiurban 1.0
94 LP001319 Male Yes 2 0 0.0 3273 1820.0 81.0 360.0 1 Urban 1.0
95 LP001322 Male No 0 1 0.0 4133 0.0 122.0 360.0 1 Semiurban 1.0
96 LP001325 Male No 0 0 0.0 3620 0.0 25.0 120.0 1 Semiurban 1.0
97 LP001326 Male No 0 1 0.0 6782 0.0 0.0 360.0 0 Urban 0.0
98 LP001327 Female Yes 0 1 0.0 2484 2302.0 137.0 360.0 1 Semiurban 1.0
99 LP001333 Male Yes 0 1 0.0 1977 997.0 50.0 360.0 1 Semiurban 1.0
100 LP001334 Male Yes 0 0 0.0 4188 0.0 115.0 180.0 1 Semiurban 1.0
101 LP001343 Male Yes 0 1 0.0 1759 3541.0 131.0 360.0 1 Semiurban 1.0
102 LP001345 Male Yes 2 0 0.0 4288 3263.0 133.0 180.0 1 Urban 1.0
103 LP001349 Male No 0 1 0.0 4843 3806.0 151.0 360.0 1 Semiurban 1.0
104 LP001350 Male Yes 1 0.0 13650 0.0 0.0 360.0 1 Urban 1.0
105 LP001356 Male Yes 0 1 0.0 4652 3583.0 0.0 360.0 1 Semiurban 1.0
106 LP001357 Male 1 0.0 3816 754.0 160.0 360.0 1 Urban 1.0
107 LP001367 Male Yes 1 1 0.0 3052 1030.0 100.0 360.0 1 Urban 1.0
108 LP001369 Male Yes 2 1 0.0 11417 1126.0 225.0 360.0 1 Urban 1.0
109 LP001370 Male No 0 0 0.0 7333 0.0 120.0 360.0 1 Rural 0.0
110 LP001379 Male Yes 2 1 0.0 3800 3600.0 216.0 360.0 0 Urban 0.0
111 LP001384 Male Yes 3+ 0 0.0 2071 754.0 94.0 480.0 1 Semiurban 1.0
112 LP001385 Male No 0 1 0.0 5316 0.0 136.0 360.0 1 Urban 1.0
113 LP001387 Female Yes 0 1 0.0 2929 2333.0 139.0 360.0 1 Semiurban 1.0
114 LP001391 Male Yes 0 0 0.0 3572 4114.0 152.0 0 Rural 0.0
115 LP001392 Female No 1 1 1.0 7451 0.0 0.0 360.0 1 Semiurban 1.0
116 LP001398 Male No 0 1 0.0 5050 0.0 118.0 360.0 1 Semiurban 1.0
117 LP001401 Male Yes 1 1 0.0 14583 0.0 185.0 180.0 1 Rural 1.0
118 LP001404 Female Yes 0 1 0.0 3167 2283.0 154.0 360.0 1 Semiurban 1.0
119 LP001405 Male Yes 1 1 0.0 2214 1398.0 85.0 360.0 0 Urban 1.0
120 LP001421 Male Yes 0 1 0.0 5568 2142.0 175.0 360.0 1 Rural 0.0
121 LP001422 Female No 0 1 0.0 10408 0.0 259.0 360.0 1 Urban 1.0
122 LP001426 Male Yes 1 0.0 5667 2667.0 180.0 360.0 1 Rural 1.0
123 LP001430 Female No 0 1 0.0 4166 0.0 44.0 360.0 1 Semiurban 1.0
124 LP001431 Female No 0 1 0.0 2137 8980.0 137.0 360.0 0 Semiurban 1.0
125 LP001432 Male Yes 2 1 0.0 2957 0.0 81.0 360.0 1 Semiurban 1.0
126 LP001439 Male Yes 0 0 0.0 4300 2014.0 194.0 360.0 1 Rural 1.0
127 LP001443 Female No 0 1 0.0 3692 0.0 93.0 360.0 0 Rural 1.0
128 LP001448 Yes 3+ 1 0.0 23803 0.0 370.0 360.0 1 Rural 1.0
129 LP001449 Male No 0 1 0.0 3865 1640.0 0.0 360.0 1 Rural 1.0
130 LP001451 Male Yes 1 1 1.0 10513 3850.0 160.0 180.0 0 Urban 0.0
131 LP001465 Male Yes 0 1 0.0 6080 2569.0 182.0 360.0 0 Rural 0.0
132 LP001469 Male No 0 1 1.0 20166 0.0 650.0 480.0 0 Urban 1.0
133 LP001473 Male No 0 1 0.0 2014 1929.0 74.0 360.0 1 Urban 1.0
134 LP001478 Male No 0 1 0.0 2718 0.0 70.0 360.0 1 Semiurban 1.0
135 LP001482 Male Yes 0 1 1.0 3459 0.0 25.0 120.0 1 Semiurban 1.0
136 LP001487 Male No 0 1 0.0 4895 0.0 102.0 360.0 1 Semiurban 1.0
137 LP001488 Male Yes 3+ 1 0.0 4000 7750.0 290.0 360.0 1 Semiurban 0.0
138 LP001489 Female Yes 0 1 0.0 4583 0.0 84.0 360.0 1 Rural 0.0
139 LP001491 Male Yes 2 1 1.0 3316 3500.0 88.0 360.0 1 Urban 1.0
140 LP001492 Male No 0 1 0.0 14999 0.0 242.0 360.0 0 Semiurban 0.0
141 LP001493 Male Yes 2 0 0.0 4200 1430.0 129.0 360.0 1 Rural 0.0
142 LP001497 Male Yes 2 1 0.0 5042 2083.0 185.0 360.0 1 Rural 0.0
143 LP001498 Male No 0 1 0.0 5417 0.0 168.0 360.0 1 Urban 1.0
144 LP001504 Male No 0 1 1.0 6950 0.0 175.0 180.0 1 Semiurban 1.0
145 LP001507 Male Yes 0 1 0.0 2698 2034.0 122.0 360.0 1 Semiurban 1.0
146 LP001508 Male Yes 2 1 0.0 11757 0.0 187.0 180.0 1 Urban 1.0
147 LP001514 Female Yes 0 1 0.0 2330 4486.0 100.0 360.0 1 Semiurban 1.0
148 LP001516 Female Yes 2 1 0.0 14866 0.0 70.0 360.0 1 Urban 1.0
149 LP001518 Male Yes 1 1 0.0 1538 1425.0 30.0 360.0 1 Urban 1.0
150 LP001519 Female No 0 1 0.0 10000 1666.0 225.0 360.0 1 Rural 0.0
151 LP001520 Male Yes 0 1 0.0 4860 830.0 125.0 360.0 1 Semiurban 1.0
152 LP001528 Male No 0 1 0.0 6277 0.0 118.0 360.0 0 Rural 0.0
153 LP001529 Male Yes 0 1 1.0 2577 3750.0 152.0 360.0 1 Rural 1.0
154 LP001531 Male No 0 1 0.0 9166 0.0 244.0 360.0 1 Urban 0.0
155 LP001532 Male Yes 2 0 0.0 2281 0.0 113.0 360.0 1 Rural 0.0
156 LP001535 Male No 0 1 0.0 3254 0.0 50.0 360.0 1 Urban 1.0
157 LP001536 Male Yes 3+ 1 0.0 39999 0.0 600.0 180.0 0 Semiurban 1.0
158 LP001541 Male Yes 1 1 0.0 6000 0.0 160.0 360.0 0 Rural 1.0
159 LP001543 Male Yes 1 1 0.0 9538 0.0 187.0 360.0 1 Urban 1.0
160 LP001546 Male No 0 1 0.0 2980 2083.0 120.0 360.0 1 Rural 1.0
161 LP001552 Male Yes 0 1 0.0 4583 5625.0 255.0 360.0 1 Semiurban 1.0
162 LP001560 Male Yes 0 0 0.0 1863 1041.0 98.0 360.0 1 Semiurban 1.0
163 LP001562 Male Yes 0 1 0.0 7933 0.0 275.0 360.0 1 Urban 0.0
164 LP001565 Male Yes 1 1 0.0 3089 1280.0 121.0 360.0 0 Semiurban 0.0
165 LP001570 Male Yes 2 1 0.0 4167 1447.0 158.0 360.0 1 Rural 1.0
166 LP001572 Male Yes 0 1 0.0 9323 0.0 75.0 180.0 1 Urban 1.0
167 LP001574 Male Yes 0 1 0.0 3707 3166.0 182.0 1 Rural 1.0
168 LP001577 Female Yes 0 1 0.0 4583 0.0 112.0 360.0 1 Rural 0.0
169 LP001578 Male Yes 0 1 0.0 2439 3333.0 129.0 360.0 1 Rural 1.0
170 LP001579 Male No 0 1 0.0 2237 0.0 63.0 480.0 0 Semiurban 0.0
171 LP001580 Male Yes 2 1 0.0 8000 0.0 200.0 360.0 1 Semiurban 1.0
172 LP001581 Male Yes 0 0 0.0 1820 1769.0 95.0 360.0 1 Rural 1.0
173 LP001585 Yes 3+ 1 0.0 51763 0.0 700.0 300.0 1 Urban 1.0
174 LP001586 Male Yes 3+ 0 0.0 3522 0.0 81.0 180.0 1 Rural 0.0
175 LP001594 Male Yes 0 1 0.0 5708 5625.0 187.0 360.0 1 Semiurban 1.0
176 LP001603 Male Yes 0 0 1.0 4344 736.0 87.0 360.0 1 Semiurban 0.0
177 LP001606 Male Yes 0 1 0.0 3497 1964.0 116.0 360.0 1 Rural 1.0
178 LP001608 Male Yes 2 1 0.0 2045 1619.0 101.0 360.0 1 Rural 1.0
179 LP001610 Male Yes 3+ 1 0.0 5516 11300.0 495.0 360.0 0 Semiurban 0.0
180 LP001616 Male Yes 1 1 0.0 3750 0.0 116.0 360.0 1 Semiurban 1.0
181 LP001630 Male No 0 0 0.0 2333 1451.0 102.0 480.0 0 Urban 0.0
182 LP001633 Male Yes 1 1 0.0 6400 7250.0 180.0 360.0 0 Urban 0.0
183 LP001634 Male No 0 1 0.0 1916 5063.0 67.0 360.0 0 Rural 0.0
184 LP001636 Male Yes 0 1 0.0 4600 0.0 73.0 180.0 1 Semiurban 1.0
185 LP001637 Male Yes 1 1 0.0 33846 0.0 260.0 360.0 1 Semiurban 0.0
186 LP001639 Female Yes 0 1 0.0 3625 0.0 108.0 360.0 1 Semiurban 1.0
187 LP001640 Male Yes 0 1 1.0 39147 4750.0 120.0 360.0 1 Semiurban 1.0
188 LP001641 Male Yes 1 1 1.0 2178 0.0 66.0 300.0 0 Rural 0.0
189 LP001643 Male Yes 0 1 0.0 2383 2138.0 58.0 360.0 0 Rural 1.0
190 LP001644 Yes 0 1 1.0 674 5296.0 168.0 360.0 1 Rural 1.0
191 LP001647 Male Yes 0 1 0.0 9328 0.0 188.0 180.0 1 Rural 1.0
192 LP001653 Male No 0 0 0.0 4885 0.0 48.0 360.0 1 Rural 1.0
193 LP001656 Male No 0 1 0.0 12000 0.0 164.0 360.0 1 Semiurban 0.0
194 LP001657 Male Yes 0 0 0.0 6033 0.0 160.0 360.0 1 Urban 0.0
195 LP001658 Male No 0 1 0.0 3858 0.0 76.0 360.0 1 Semiurban 1.0
196 LP001664 Male No 0 1 0.0 4191 0.0 120.0 360.0 1 Rural 1.0
197 LP001665 Male Yes 1 1 0.0 3125 2583.0 170.0 360.0 1 Semiurban 0.0
198 LP001666 Male No 0 1 0.0 8333 3750.0 187.0 360.0 1 Rural 1.0
199 LP001669 Female No 0 0 0.0 1907 2365.0 120.0 1 Urban 1.0
200 LP001671 Female Yes 0 1 0.0 3416 2816.0 113.0 360.0 0 Semiurban 1.0
201 LP001673 Male No 0 1 1.0 11000 0.0 83.0 360.0 1 Urban 0.0
202 LP001674 Male Yes 1 0 0.0 2600 2500.0 90.0 360.0 1 Semiurban 1.0
203 LP001677 Male No 2 1 0.0 4923 0.0 166.0 360.0 0 Semiurban 1.0
204 LP001682 Male Yes 3+ 0 0.0 3992 0.0 0.0 180.0 1 Urban 0.0
205 LP001688 Male Yes 1 0 0.0 3500 1083.0 135.0 360.0 1 Urban 1.0
206 LP001691 Male Yes 2 0 0.0 3917 0.0 124.0 360.0 1 Semiurban 1.0
207 LP001692 Female No 0 0 0.0 4408 0.0 120.0 360.0 1 Semiurban 1.0
208 LP001693 Female No 0 1 0.0 3244 0.0 80.0 360.0 1 Urban 1.0
209 LP001698 Male No 0 0 0.0 3975 2531.0 55.0 360.0 1 Rural 1.0
210 LP001699 Male No 0 1 0.0 2479 0.0 59.0 360.0 1 Urban 1.0
211 LP001702 Male No 0 1 0.0 3418 0.0 127.0 360.0 1 Semiurban 0.0
212 LP001708 Female No 0 1 0.0 10000 0.0 214.0 360.0 1 Semiurban 0.0
213 LP001711 Male Yes 3+ 1 0.0 3430 1250.0 128.0 360.0 0 Semiurban 0.0
214 LP001713 Male Yes 1 1 1.0 7787 0.0 240.0 360.0 1 Urban 1.0
215 LP001715 Male Yes 3+ 0 1.0 5703 0.0 130.0 360.0 1 Rural 1.0
216 LP001716 Male Yes 0 1 0.0 3173 3021.0 137.0 360.0 1 Urban 1.0
217 LP001720 Male Yes 3+ 0 0.0 3850 983.0 100.0 360.0 1 Semiurban 1.0
218 LP001722 Male Yes 0 1 0.0 150 1800.0 135.0 360.0 1 Rural 0.0
219 LP001726 Male Yes 0 1 0.0 3727 1775.0 131.0 360.0 1 Semiurban 1.0
220 LP001732 Male Yes 2 1 0.0 5000 0.0 72.0 360.0 0 Semiurban 0.0
221 LP001734 Female Yes 2 1 0.0 4283 2383.0 127.0 360.0 0 Semiurban 1.0
222 LP001736 Male Yes 0 1 0.0 2221 0.0 60.0 360.0 0 Urban 0.0
223 LP001743 Male Yes 2 1 0.0 4009 1717.0 116.0 360.0 1 Semiurban 1.0
224 LP001744 Male No 0 1 0.0 2971 2791.0 144.0 360.0 1 Semiurban 1.0
225 LP001749 Male Yes 0 1 0.0 7578 1010.0 175.0 1 Semiurban 1.0
226 LP001750 Male Yes 0 1 0.0 6250 0.0 128.0 360.0 1 Semiurban 1.0
227 LP001751 Male Yes 0 1 0.0 3250 0.0 170.0 360.0 1 Rural 0.0
228 LP001754 Male Yes 0 1.0 4735 0.0 138.0 360.0 1 Urban 0.0
229 LP001758 Male Yes 2 1 0.0 6250 1695.0 210.0 360.0 1 Semiurban 1.0
230 LP001760 Male 1 0.0 4758 0.0 158.0 480.0 1 Semiurban 1.0
231 LP001761 Male No 0 1 1.0 6400 0.0 200.0 360.0 1 Rural 1.0
232 LP001765 Male Yes 1 1 0.0 2491 2054.0 104.0 360.0 1 Semiurban 1.0
233 LP001768 Male Yes 0 1 0.0 3716 0.0 42.0 180.0 1 Rural 1.0
234 LP001770 Male No 0 0 0.0 3189 2598.0 120.0 1 Rural 1.0
235 LP001776 Female No 0 1 0.0 8333 0.0 280.0 360.0 1 Semiurban 1.0
236 LP001778 Male Yes 1 1 0.0 3155 1779.0 140.0 360.0 1 Semiurban 1.0
237 LP001784 Male Yes 1 1 0.0 5500 1260.0 170.0 360.0 1 Rural 1.0
238 LP001786 Male Yes 0 1 0.0 5746 0.0 255.0 360.0 0 Urban 0.0
239 LP001788 Female No 0 1 1.0 3463 0.0 122.0 360.0 0 Urban 1.0
240 LP001790 Female No 1 1 0.0 3812 0.0 112.0 360.0 1 Rural 1.0
241 LP001792 Male Yes 1 1 0.0 3315 0.0 96.0 360.0 1 Semiurban 1.0
242 LP001798 Male Yes 2 1 0.0 5819 5000.0 120.0 360.0 1 Rural 1.0
243 LP001800 Male Yes 1 0 0.0 2510 1983.0 140.0 180.0 1 Urban 0.0
244 LP001806 Male No 0 1 0.0 2965 5701.0 155.0 60.0 1 Urban 1.0
245 LP001807 Male Yes 2 1 1.0 6250 1300.0 108.0 360.0 1 Rural 1.0
246 LP001811 Male Yes 0 0 0.0 3406 4417.0 123.0 360.0 1 Semiurban 1.0
247 LP001813 Male No 0 1 1.0 6050 4333.0 120.0 180.0 1 Urban 0.0
248 LP001814 Male Yes 2 1 0.0 9703 0.0 112.0 360.0 1 Urban 1.0
249 LP001819 Male Yes 1 0 0.0 6608 0.0 137.0 180.0 1 Urban 1.0
250 LP001824 Male Yes 1 1 0.0 2882 1843.0 123.0 480.0 1 Semiurban 1.0
251 LP001825 Male Yes 0 1 0.0 1809 1868.0 90.0 360.0 1 Urban 1.0
252 LP001835 Male Yes 0 0 0.0 1668 3890.0 201.0 360.0 0 Semiurban 0.0
253 LP001836 Female No 2 1 0.0 3427 0.0 138.0 360.0 1 Urban 0.0
254 LP001841 Male No 0 0 1.0 2583 2167.0 104.0 360.0 1 Rural 1.0
255 LP001843 Male Yes 1 0 0.0 2661 7101.0 279.0 180.0 1 Semiurban 1.0
256 LP001844 Male No 0 1 1.0 16250 0.0 192.0 360.0 0 Urban 0.0
257 LP001846 Female No 3+ 1 0.0 3083 0.0 255.0 360.0 1 Rural 1.0
258 LP001849 Male No 0 0 0.0 6045 0.0 115.0 360.0 0 Rural 0.0
259 LP001854 Male Yes 3+ 1 0.0 5250 0.0 94.0 360.0 1 Urban 0.0
260 LP001859 Male Yes 0 1 0.0 14683 2100.0 304.0 360.0 1 Rural 0.0
261 LP001864 Male Yes 3+ 0 0.0 4931 0.0 128.0 360.0 0 Semiurban 0.0
262 LP001865 Male Yes 1 1 0.0 6083 4250.0 330.0 360.0 0 Urban 1.0
263 LP001868 Male No 0 1 0.0 2060 2209.0 134.0 360.0 1 Semiurban 1.0
264 LP001870 Female No 1 1 0.0 3481 0.0 155.0 36.0 1 Semiurban 0.0
265 LP001871 Female No 0 1 0.0 7200 0.0 120.0 360.0 1 Rural 1.0
266 LP001872 Male No 0 1 1.0 5166 0.0 128.0 360.0 1 Semiurban 1.0
267 LP001875 Male No 0 1 0.0 4095 3447.0 151.0 360.0 1 Rural 1.0
268 LP001877 Male Yes 2 1 0.0 4708 1387.0 150.0 360.0 1 Semiurban 1.0
269 LP001882 Male Yes 3+ 1 0.0 4333 1811.0 160.0 360.0 0 Urban 1.0
270 LP001883 Female No 0 1 0.0 3418 0.0 135.0 360.0 1 Rural 0.0
271 LP001884 Female No 1 1 0.0 2876 1560.0 90.0 360.0 1 Urban 1.0
272 LP001888 Female No 0 1 0.0 3237 0.0 30.0 360.0 1 Urban 1.0
273 LP001891 Male Yes 0 1 0.0 11146 0.0 136.0 360.0 1 Urban 1.0
274 LP001892 Male No 0 1 0.0 2833 1857.0 126.0 360.0 1 Rural 1.0
275 LP001894 Male Yes 0 1 0.0 2620 2223.0 150.0 360.0 1 Semiurban 1.0
276 LP001896 Male Yes 2 1 0.0 3900 0.0 90.0 360.0 1 Semiurban 1.0
277 LP001900 Male Yes 1 1 0.0 2750 1842.0 115.0 360.0 1 Semiurban 1.0
278 LP001903 Male Yes 0 1 0.0 3993 3274.0 207.0 360.0 1 Semiurban 1.0
279 LP001904 Male Yes 0 1 0.0 3103 1300.0 80.0 360.0 1 Urban 1.0
280 LP001907 Male Yes 0 1 0.0 14583 0.0 436.0 360.0 1 Semiurban 1.0
281 LP001908 Female Yes 0 0 0.0 4100 0.0 124.0 360.0 0 Rural 1.0
282 LP001910 Male No 1 0 1.0 4053 2426.0 158.0 360.0 0 Urban 0.0
283 LP001914 Male Yes 0 1 0.0 3927 800.0 112.0 360.0 1 Semiurban 1.0
284 LP001915 Male Yes 2 1 0.0 2301 985.7999878 78.0 180.0 1 Urban 1.0
285 LP001917 Female No 0 1 0.0 1811 1666.0 54.0 360.0 1 Urban 1.0
286 LP001922 Male Yes 0 1 0.0 20667 0.0 0.0 360.0 1 Rural 0.0
287 LP001924 Male No 0 1 0.0 3158 3053.0 89.0 360.0 1 Rural 1.0
288 LP001925 Female No 0 1 1.0 2600 1717.0 99.0 300.0 1 Semiurban 0.0
289 LP001926 Male Yes 0 1 0.0 3704 2000.0 120.0 360.0 1 Rural 1.0
290 LP001931 Female No 0 1 0.0 4124 0.0 115.0 360.0 1 Semiurban 1.0
291 LP001935 Male No 0 1 0.0 9508 0.0 187.0 360.0 1 Rural 1.0
292 LP001936 Male Yes 0 1 0.0 3075 2416.0 139.0 360.0 1 Rural 1.0
293 LP001938 Male Yes 2 1 0.0 4400 0.0 127.0 360.0 0 Semiurban 0.0
294 LP001940 Male Yes 2 1 0.0 3153 1560.0 134.0 360.0 1 Urban 1.0
295 LP001945 Female No 1 0.0 5417 0.0 143.0 480.0 0 Urban 0.0
296 LP001947 Male Yes 0 1 0.0 2383 3334.0 172.0 360.0 1 Semiurban 1.0
297 LP001949 Male Yes 3+ 1 0.0 4416 1250.0 110.0 360.0 1 Urban 1.0
298 LP001953 Male Yes 1 1 0.0 6875 0.0 200.0 360.0 1 Semiurban 1.0
299 LP001954 Female Yes 1 1 0.0 4666 0.0 135.0 360.0 1 Urban 1.0
300 LP001955 Female No 0 1 0.0 5000 2541.0 151.0 480.0 1 Rural 0.0
301 LP001963 Male Yes 1 1 0.0 2014 2925.0 113.0 360.0 1 Urban 0.0
302 LP001964 Male Yes 0 0 0.0 1800 2934.0 93.0 360.0 0 Urban 0.0
303 LP001972 Male Yes 0 0.0 2875 1750.0 105.0 360.0 1 Semiurban 1.0
304 LP001974 Female No 0 1 0.0 5000 0.0 132.0 360.0 1 Rural 1.0
305 LP001977 Male Yes 1 1 0.0 1625 1803.0 96.0 360.0 1 Urban 1.0
306 LP001978 Male No 0 1 0.0 4000 2500.0 140.0 360.0 1 Rural 1.0
307 LP001990 Male No 0 0 0.0 2000 0.0 0.0 360.0 1 Urban 0.0
308 LP001993 Female No 0 1 0.0 3762 1666.0 135.0 360.0 1 Rural 1.0
309 LP001994 Female No 0 1 0.0 2400 1863.0 104.0 360.0 0 Urban 0.0
310 LP001996 Male No 0 1 0.0 20233 0.0 480.0 360.0 1 Rural 0.0
311 LP001998 Male Yes 2 0 0.0 7667 0.0 185.0 360.0 0 Rural 1.0
312 LP002002 Female No 0 1 0.0 2917 0.0 84.0 360.0 1 Semiurban 1.0
313 LP002004 Male No 0 0 0.0 2927 2405.0 111.0 360.0 1 Semiurban 1.0
314 LP002006 Female No 0 1 0.0 2507 0.0 56.0 360.0 1 Rural 1.0
315 LP002008 Male Yes 2 1 1.0 5746 0.0 144.0 84.0 0 Rural 1.0
316 LP002024 Yes 0 1 0.0 2473 1843.0 159.0 360.0 1 Rural 0.0
317 LP002031 Male Yes 1 0 0.0 3399 1640.0 111.0 180.0 1 Urban 1.0
318 LP002035 Male Yes 2 1 0.0 3717 0.0 120.0 360.0 1 Semiurban 1.0
319 LP002036 Male Yes 0 1 0.0 2058 2134.0 88.0 360.0 0 Urban 1.0
320 LP002043 Female No 1 1 0.0 3541 0.0 112.0 360.0 0 Semiurban 1.0
321 LP002050 Male Yes 1 1 1.0 10000 0.0 155.0 360.0 1 Rural 0.0
322 LP002051 Male Yes 0 1 0.0 2400 2167.0 115.0 360.0 1 Semiurban 1.0
323 LP002053 Male Yes 3+ 1 0.0 4342 189.0 124.0 360.0 1 Semiurban 1.0
324 LP002054 Male Yes 2 0 0.0 3601 1590.0 0.0 360.0 1 Rural 1.0
325 LP002055 Female No 0 1 0.0 3166 2985.0 132.0 360.0 0 Rural 1.0
326 LP002065 Male Yes 3+ 1 0.0 15000 0.0 300.0 360.0 1 Rural 1.0
327 LP002067 Male Yes 1 1 1.0 8666 4983.0 376.0 360.0 0 Rural 0.0
328 LP002068 Male No 0 1 0.0 4917 0.0 130.0 360.0 0 Rural 1.0
329 LP002082 Male Yes 0 1 1.0 5818 2160.0 184.0 360.0 1 Semiurban 1.0
330 LP002086 Female Yes 0 1 0.0 4333 2451.0 110.0 360.0 1 Urban 0.0
331 LP002087 Female No 0 1 0.0 2500 0.0 67.0 360.0 1 Urban 1.0
332 LP002097 Male No 1 1 0.0 4384 1793.0 117.0 360.0 1 Urban 1.0
333 LP002098 Male No 0 1 0.0 2935 0.0 98.0 360.0 1 Semiurban 1.0
334 LP002100 Male No 1 0.0 2833 0.0 71.0 360.0 1 Urban 1.0
335 LP002101 Male Yes 0 1 0.0 63337 0.0 490.0 180.0 1 Urban 1.0
336 LP002103 Yes 1 1 1.0 9833 1833.0 182.0 180.0 1 Urban 1.0
337 LP002106 Male Yes 1 1.0 5503 4490.0 70.0 1 Semiurban 1.0
338 LP002110 Male Yes 1 1 0.0 5250 688.0 160.0 360.0 1 Rural 1.0
339 LP002112 Male Yes 2 1 1.0 2500 4600.0 176.0 360.0 1 Rural 1.0
340 LP002113 Female No 3+ 0 0.0 1830 0.0 0.0 360.0 0 Urban 0.0
341 LP002114 Female No 0 1 0.0 4160 0.0 71.0 360.0 1 Semiurban 1.0
342 LP002115 Male Yes 3+ 0 0.0 2647 1587.0 173.0 360.0 1 Rural 0.0
343 LP002116 Female No 0 1 0.0 2378 0.0 46.0 360.0 1 Rural 0.0
344 LP002119 Male Yes 1 0 0.0 4554 1229.0 158.0 360.0 1 Urban 1.0
345 LP002126 Male Yes 3+ 0 0.0 3173 0.0 74.0 360.0 1 Semiurban 1.0
346 LP002128 Male Yes 2 1 0.0 2583 2330.0 125.0 360.0 1 Rural 1.0
347 LP002129 Male Yes 0 1 0.0 2499 2458.0 160.0 360.0 1 Semiurban 1.0
348 LP002130 Male Yes 0 0.0 3523 3230.0 152.0 360.0 0 Rural 0.0
349 LP002131 Male Yes 2 0 0.0 3083 2168.0 126.0 360.0 1 Urban 1.0
350 LP002137 Male Yes 0 1 0.0 6333 4583.0 259.0 360.0 0 Semiurban 1.0
351 LP002138 Male Yes 0 1 0.0 2625 6250.0 187.0 360.0 1 Rural 1.0
352 LP002139 Male Yes 0 1 0.0 9083 0.0 228.0 360.0 1 Semiurban 1.0
353 LP002140 Male No 0 1 0.0 8750 4167.0 308.0 360.0 1 Rural 0.0
354 LP002141 Male Yes 3+ 1 0.0 2666 2083.0 95.0 360.0 1 Rural 1.0
355 LP002142 Female Yes 0 1 1.0 5500 0.0 105.0 360.0 0 Rural 0.0
356 LP002143 Female Yes 0 1 0.0 2423 505.0 130.0 360.0 1 Semiurban 1.0
357 LP002144 Female No 1 0.0 3813 0.0 116.0 180.0 1 Urban 1.0
358 LP002149 Male Yes 2 1 0.0 8333 3167.0 165.0 360.0 1 Rural 1.0
359 LP002151 Male Yes 1 1 0.0 3875 0.0 67.0 360.0 1 Urban 0.0
360 LP002158 Male Yes 0 0 0.0 3000 1666.0 100.0 480.0 0 Urban 0.0
361 LP002160 Male Yes 3+ 1 0.0 5167 3167.0 200.0 360.0 1 Semiurban 1.0
362 LP002161 Female No 1 1 0.0 4723 0.0 81.0 360.0 1 Semiurban 0.0
363 LP002170 Male Yes 2 1 0.0 5000 3667.0 236.0 360.0 1 Semiurban 1.0
364 LP002175 Male Yes 0 1 0.0 4750 2333.0 130.0 360.0 1 Urban 1.0
365 LP002178 Male Yes 0 1 0.0 3013 3033.0 95.0 300.0 0 Urban 1.0
366 LP002180 Male No 0 1 1.0 6822 0.0 141.0 360.0 1 Rural 1.0
367 LP002181 Male No 0 0 0.0 6216 0.0 133.0 360.0 1 Rural 0.0
368 LP002187 Male No 0 1 0.0 2500 0.0 96.0 480.0 1 Semiurban 0.0
369 LP002188 Male No 0 1 0.0 5124 0.0 124.0 0 Rural 0.0
370 LP002190 Male Yes 1 1 0.0 6325 0.0 175.0 360.0 1 Semiurban 1.0
371 LP002191 Male Yes 0 1 0.0 19730 5266.0 570.0 360.0 1 Rural 0.0
372 LP002194 Female No 0 1 1.0 15759 0.0 55.0 360.0 1 Semiurban 1.0
373 LP002197 Male Yes 2 1 0.0 5185 0.0 155.0 360.0 1 Semiurban 1.0
374 LP002201 Male Yes 2 1 1.0 9323 7873.0 380.0 300.0 1 Rural 1.0
375 LP002205 Male No 1 1 0.0 3062 1987.0 111.0 180.0 0 Urban 0.0
376 LP002209 Female No 0 1 0.0 2764 1459.0 110.0 360.0 1 Urban 1.0
377 LP002211 Male Yes 0 1 0.0 4817 923.0 120.0 180.0 1 Urban 1.0
378 LP002219 Male Yes 3+ 1 0.0 8750 4996.0 130.0 360.0 1 Rural 1.0
379 LP002223 Male Yes 0 1 0.0 4310 0.0 130.0 360.0 0 Semiurban 1.0
380 LP002224 Male No 0 1 0.0 3069 0.0 71.0 480.0 1 Urban 0.0
381 LP002225 Male Yes 2 1 0.0 5391 0.0 130.0 360.0 1 Urban 1.0
382 LP002226 Male Yes 0 1 0.0 3333 2500.0 128.0 360.0 1 Semiurban 1.0
383 LP002229 Male No 0 1 0.0 5941 4232.0 296.0 360.0 1 Semiurban 1.0
384 LP002231 Female No 0 1 0.0 6000 0.0 156.0 360.0 1 Urban 1.0
385 LP002234 Male No 0 1 1.0 7167 0.0 128.0 360.0 1 Urban 1.0
386 LP002236 Male Yes 2 1 0.0 4566 0.0 100.0 360.0 1 Urban 0.0
387 LP002237 Male No 1 1 0.0 3667 0.0 113.0 180.0 1 Urban 1.0
388 LP002239 Male No 0 0 0.0 2346 1600.0 132.0 360.0 1 Semiurban 1.0
389 LP002243 Male Yes 0 0 0.0 3010 3136.0 0.0 360.0 0 Urban 0.0
390 LP002244 Male Yes 0 1 0.0 2333 2417.0 136.0 360.0 1 Urban 1.0
391 LP002250 Male Yes 0 1 0.0 5488 0.0 125.0 360.0 1 Rural 1.0
392 LP002255 Male No 3+ 1 0.0 9167 0.0 185.0 360.0 1 Rural 1.0
393 LP002262 Male Yes 3+ 1 0.0 9504 0.0 275.0 360.0 1 Rural 1.0
394 LP002263 Male Yes 0 1 0.0 2583 2115.0 120.0 360.0 0 Urban 1.0
395 LP002265 Male Yes 2 0 0.0 1993 1625.0 113.0 180.0 1 Semiurban 1.0
396 LP002266 Male Yes 2 1 0.0 3100 1400.0 113.0 360.0 1 Urban 1.0
397 LP002272 Male Yes 2 1 0.0 3276 484.0 135.0 360.0 0 Semiurban 1.0
398 LP002277 Female No 0 1 0.0 3180 0.0 71.0 360.0 0 Urban 0.0
399 LP002281 Male Yes 0 1 0.0 3033 1459.0 95.0 360.0 1 Urban 1.0
400 LP002284 Male No 0 0 0.0 3902 1666.0 109.0 360.0 1 Rural 1.0
401 LP002287 Female No 0 1 0.0 1500 1800.0 103.0 360.0 0 Semiurban 0.0
402 LP002288 Male Yes 2 0 0.0 2889 0.0 45.0 180.0 0 Urban 0.0
403 LP002296 Male No 0 0 0.0 2755 0.0 65.0 300.0 1 Rural 0.0
404 LP002297 Male No 0 1 0.0 2500 20000.0 103.0 360.0 1 Semiurban 1.0
405 LP002300 Female No 0 0 0.0 1963 0.0 53.0 360.0 1 Semiurban 1.0
406 LP002301 Female No 0 1 1.0 7441 0.0 194.0 360.0 1 Rural 0.0
407 LP002305 Female No 0 1 0.0 4547 0.0 115.0 360.0 1 Semiurban 1.0
408 LP002308 Male Yes 0 0 0.0 2167 2400.0 115.0 360.0 1 Urban 1.0
409 LP002314 Female No 0 0 0.0 2213 0.0 66.0 360.0 1 Rural 1.0
410 LP002315 Male Yes 1 1 0.0 8300 0.0 152.0 300.0 0 Semiurban 0.0
411 LP002317 Male Yes 3+ 1 0.0 81000 0.0 360.0 360.0 0 Rural 0.0
412 LP002318 Female No 1 0 1.0 3867 0.0 62.0 360.0 1 Semiurban 0.0
413 LP002319 Male Yes 0 1 0.0 6256 0.0 160.0 360.0 0 Urban 1.0
414 LP002328 Male Yes 0 0 0.0 6096 0.0 218.0 360.0 0 Rural 0.0
415 LP002332 Male Yes 0 0 0.0 2253 2033.0 110.0 360.0 1 Rural 1.0
416 LP002335 Female Yes 0 0 0.0 2149 3237.0 178.0 360.0 0 Semiurban 0.0
417 LP002337 Female No 0 1 0.0 2995 0.0 60.0 360.0 1 Urban 1.0
418 LP002341 Female No 1 1 0.0 2600 0.0 160.0 360.0 1 Urban 0.0
419 LP002342 Male Yes 2 1 1.0 1600 20000.0 239.0 360.0 1 Urban 0.0
420 LP002345 Male Yes 0 1 0.0 1025 2773.0 112.0 360.0 1 Rural 1.0
421 LP002347 Male Yes 0 1 0.0 3246 1417.0 138.0 360.0 1 Semiurban 1.0
422 LP002348 Male Yes 0 1 0.0 5829 0.0 138.0 360.0 1 Rural 1.0
423 LP002357 Female No 0 0 0.0 2720 0.0 80.0 0 Urban 0.0
424 LP002361 Male Yes 0 1 0.0 1820 1719.0 100.0 360.0 1 Urban 1.0
425 LP002362 Male Yes 1 1 0.0 7250 1667.0 110.0 0 Urban 0.0
426 LP002364 Male Yes 0 1 0.0 14880 0.0 96.0 360.0 1 Semiurban 1.0
427 LP002366 Male Yes 0 1 0.0 2666 4300.0 121.0 360.0 1 Rural 1.0
428 LP002367 Female No 1 0 0.0 4606 0.0 81.0 360.0 1 Rural 0.0
429 LP002368 Male Yes 2 1 0.0 5935 0.0 133.0 360.0 1 Semiurban 1.0
430 LP002369 Male Yes 0 1 0.0 2920 16.12000084 87.0 360.0 1 Rural 1.0
431 LP002370 Male No 0 0 0.0 2717 0.0 60.0 180.0 1 Urban 1.0
432 LP002377 Female No 1 1 1.0 8624 0.0 150.0 360.0 1 Semiurban 1.0
433 LP002379 Male No 0 1 0.0 6500 0.0 105.0 360.0 0 Rural 0.0
434 LP002386 Male No 0 1 0.0 12876 0.0 405.0 360.0 1 Semiurban 1.0
435 LP002387 Male Yes 0 1 0.0 2425 2340.0 143.0 360.0 1 Semiurban 1.0
436 LP002390 Male No 0 1 0.0 3750 0.0 100.0 360.0 1 Urban 1.0
437 LP002393 Female 1 0.0 10047 0.0 0.0 240.0 1 Semiurban 1.0
438 LP002398 Male No 0 1 0.0 1926 1851.0 50.0 360.0 1 Semiurban 1.0
439 LP002401 Male Yes 0 1 0.0 2213 1125.0 0.0 360.0 1 Urban 1.0
440 LP002403 Male No 0 1 1.0 10416 0.0 187.0 360.0 0 Urban 0.0
441 LP002407 Female Yes 0 0 1.0 7142 0.0 138.0 360.0 1 Rural 1.0
442 LP002408 Male No 0 1 0.0 3660 5064.0 187.0 360.0 1 Semiurban 1.0
443 LP002409 Male Yes 0 1 0.0 7901 1833.0 180.0 360.0 1 Rural 1.0
444 LP002418 Male No 3+ 0 0.0 4707 1993.0 148.0 360.0 1 Semiurban 1.0
445 LP002422 Male No 1 1 0.0 37719 0.0 152.0 360.0 1 Semiurban 1.0
446 LP002424 Male Yes 0 1 0.0 7333 8333.0 175.0 300.0 0 Rural 1.0
447 LP002429 Male Yes 1 1 1.0 3466 1210.0 130.0 360.0 1 Rural 1.0
448 LP002434 Male Yes 2 0 0.0 4652 0.0 110.0 360.0 1 Rural 1.0
449 LP002435 Male Yes 0 1 0.0 3539 1376.0 55.0 360.0 1 Rural 0.0
450 LP002443 Male Yes 2 1 0.0 3340 1710.0 150.0 360.0 0 Rural 0.0
451 LP002444 Male No 1 0 1.0 2769 1542.0 190.0 360.0 0 Semiurban 0.0
452 LP002446 Male Yes 2 0 0.0 2309 1255.0 125.0 360.0 0 Rural 0.0
453 LP002447 Male Yes 2 0 0.0 1958 1456.0 60.0 300.0 0 Urban 1.0
454 LP002448 Male Yes 0 1 0.0 3948 1733.0 149.0 360.0 0 Rural 0.0
455 LP002449 Male Yes 0 1 0.0 2483 2466.0 90.0 180.0 0 Rural 1.0
456 LP002453 Male No 0 1 1.0 7085 0.0 84.0 360.0 1 Semiurban 1.0
457 LP002455 Male Yes 2 1 0.0 3859 0.0 96.0 360.0 1 Semiurban 1.0
458 LP002459 Male Yes 0 1 0.0 4301 0.0 118.0 360.0 1 Urban 1.0
459 LP002467 Male Yes 0 1 0.0 3708 2569.0 173.0 360.0 1 Urban 0.0
460 LP002472 Male No 2 1 0.0 4354 0.0 136.0 360.0 1 Rural 1.0
461 LP002473 Male Yes 0 1 0.0 8334 0.0 160.0 360.0 1 Semiurban 0.0
462 LP002478 Yes 0 1 1.0 2083 4083.0 160.0 360.0 0 Semiurban 1.0
463 LP002484 Male Yes 3+ 1 0.0 7740 0.0 128.0 180.0 1 Urban 1.0
464 LP002487 Male Yes 0 1 0.0 3015 2188.0 153.0 360.0 1 Rural 1.0
465 LP002489 Female No 1 0 0.0 5191 0.0 132.0 360.0 1 Semiurban 1.0
466 LP002493 Male No 0 1 0.0 4166 0.0 98.0 360.0 0 Semiurban 0.0
467 LP002494 Male No 0 1 0.0 6000 0.0 140.0 360.0 1 Rural 1.0
468 LP002500 Male Yes 3+ 0 0.0 2947 1664.0 70.0 180.0 0 Urban 0.0
469 LP002501 Yes 0 1 0.0 16692 0.0 110.0 360.0 1 Semiurban 1.0
470 LP002502 Female Yes 2 0 0.0 210 2917.0 98.0 360.0 1 Semiurban 1.0
471 LP002505 Male Yes 0 1 0.0 4333 2451.0 110.0 360.0 1 Urban 0.0
472 LP002515 Male Yes 1 1 1.0 3450 2079.0 162.0 360.0 1 Semiurban 1.0
473 LP002517 Male Yes 1 0 0.0 2653 1500.0 113.0 180.0 0 Rural 0.0
474 LP002519 Male Yes 3+ 1 0.0 4691 0.0 100.0 360.0 1 Semiurban 1.0
475 LP002522 Female No 0 1 1.0 2500 0.0 93.0 360.0 0 Urban 1.0
476 LP002524 Male No 2 1 0.0 5532 4648.0 162.0 360.0 1 Rural 1.0
477 LP002527 Male Yes 2 1 1.0 16525 1014.0 150.0 360.0 1 Rural 1.0
478 LP002529 Male Yes 2 1 0.0 6700 1750.0 230.0 300.0 1 Semiurban 1.0
479 LP002530 Yes 2 1 0.0 2873 1872.0 132.0 360.0 0 Semiurban 0.0
480 LP002531 Male Yes 1 1 1.0 16667 2250.0 86.0 360.0 1 Semiurban 1.0
481 LP002533 Male Yes 2 1 0.0 2947 1603.0 0.0 360.0 1 Urban 0.0
482 LP002534 Female No 0 0 0.0 4350 0.0 154.0 360.0 1 Rural 1.0
483 LP002536 Male Yes 3+ 0 0.0 3095 0.0 113.0 360.0 1 Rural 1.0
484 LP002537 Male Yes 0 1 0.0 2083 3150.0 128.0 360.0 1 Semiurban 1.0
485 LP002541 Male Yes 0 1 0.0 10833 0.0 234.0 360.0 1 Semiurban 1.0
486 LP002543 Male Yes 2 1 0.0 8333 0.0 246.0 360.0 1 Semiurban 1.0
487 LP002544 Male Yes 1 0 0.0 1958 2436.0 131.0 360.0 1 Rural 1.0
488 LP002545 Male No 2 1 0.0 3547 0.0 80.0 360.0 0 Rural 0.0
489 LP002547 Male Yes 1 1 0.0 18333 0.0 500.0 360.0 1 Urban 0.0
490 LP002555 Male Yes 2 1 1.0 4583 2083.0 160.0 360.0 1 Semiurban 1.0
491 LP002556 Male No 0 1 0.0 2435 0.0 75.0 360.0 1 Urban 0.0
492 LP002560 Male No 0 0 0.0 2699 2785.0 96.0 360.0 0 Semiurban 1.0
493 LP002562 Male Yes 1 0 0.0 5333 1131.0 186.0 360.0 0 Urban 1.0
494 LP002571 Male No 0 0 0.0 3691 0.0 110.0 360.0 1 Rural 1.0
495 LP002582 Female No 0 0 1.0 17263 0.0 225.0 360.0 1 Semiurban 1.0
496 LP002585 Male Yes 0 1 0.0 3597 2157.0 119.0 360.0 0 Rural 0.0
497 LP002586 Female Yes 1 1 0.0 3326 913.0 105.0 84.0 1 Semiurban 1.0
498 LP002587 Male Yes 0 0 0.0 2600 1700.0 107.0 360.0 1 Rural 1.0
499 LP002588 Male Yes 0 1 0.0 4625 2857.0 111.0 12.0 0 Urban 1.0
500 LP002600 Male Yes 1 1 1.0 2895 0.0 95.0 360.0 1 Semiurban 1.0
501 LP002602 Male No 0 1 0.0 6283 4416.0 209.0 360.0 0 Rural 0.0
502 LP002603 Female No 0 1 0.0 645 3683.0 113.0 480.0 1 Rural 1.0
503 LP002606 Female No 0 1 0.0 3159 0.0 100.0 360.0 1 Semiurban 1.0
504 LP002615 Male Yes 2 1 0.0 4865 5624.0 208.0 360.0 1 Semiurban 1.0
505 LP002618 Male Yes 1 0 0.0 4050 5302.0 138.0 360.0 0 Rural 0.0
506 LP002619 Male Yes 0 0 0.0 3814 1483.0 124.0 300.0 1 Semiurban 1.0
507 LP002622 Male Yes 2 1 0.0 3510 4416.0 243.0 360.0 1 Rural 1.0
508 LP002624 Male Yes 0 1 0.0 20833 6667.0 480.0 360.0 0 Urban 1.0
509 LP002625 No 0 1 0.0 3583 0.0 96.0 360.0 1 Urban 0.0
510 LP002626 Male Yes 0 1 1.0 2479 3013.0 188.0 360.0 1 Urban 1.0
511 LP002634 Female No 1 1 0.0 13262 0.0 40.0 360.0 1 Urban 1.0
512 LP002637 Male No 0 0 0.0 3598 1287.0 100.0 360.0 1 Rural 0.0
513 LP002640 Male Yes 1 1 0.0 6065 2004.0 250.0 360.0 1 Semiurban 1.0
514 LP002643 Male Yes 2 1 0.0 3283 2035.0 148.0 360.0 1 Urban 1.0
515 LP002648 Male Yes 0 1 0.0 2130 6666.0 70.0 180.0 1 Semiurban 0.0
516 LP002652 Male No 0 1 0.0 5815 3666.0 311.0 360.0 1 Rural 0.0
517 LP002659 Male Yes 3+ 1 0.0 3466 3428.0 150.0 360.0 1 Rural 1.0
518 LP002670 Female Yes 2 1 0.0 2031 1632.0 113.0 480.0 1 Semiurban 1.0
519 LP002682 Male Yes 0 0.0 3074 1800.0 123.0 360.0 0 Semiurban 0.0
520 LP002683 Male No 0 1 0.0 4683 1915.0 185.0 360.0 1 Semiurban 0.0
521 LP002684 Female No 0 0 0.0 3400 0.0 95.0 360.0 1 Rural 0.0
522 LP002689 Male Yes 2 0 0.0 2192 1742.0 45.0 360.0 1 Semiurban 1.0
523 LP002690 Male No 0 1 0.0 2500 0.0 55.0 360.0 1 Semiurban 1.0
524 LP002692 Male Yes 3+ 1 1.0 5677 1424.0 100.0 360.0 1 Rural 1.0
525 LP002693 Male Yes 2 1 1.0 7948 7166.0 480.0 360.0 1 Rural 1.0
526 LP002697 Male No 0 1 0.0 4680 2087.0 0.0 360.0 1 Semiurban 0.0
527 LP002699 Male Yes 2 1 1.0 17500 0.0 400.0 360.0 1 Rural 1.0
528 LP002705 Male Yes 0 1 0.0 3775 0.0 110.0 360.0 1 Semiurban 1.0
529 LP002706 Male Yes 1 0 0.0 5285 1430.0 161.0 360.0 0 Semiurban 1.0
530 LP002714 Male No 1 0 0.0 2679 1302.0 94.0 360.0 1 Semiurban 1.0
531 LP002716 Male No 0 0 0.0 6783 0.0 130.0 360.0 1 Semiurban 1.0
532 LP002717 Male Yes 0 1 0.0 1025 5500.0 216.0 360.0 0 Rural 1.0
533 LP002720 Male Yes 3+ 1 0.0 4281 0.0 100.0 360.0 1 Urban 1.0
534 LP002723 Male No 2 1 0.0 3588 0.0 110.0 360.0 0 Rural 0.0
535 LP002729 Male No 1 1 0.0 11250 0.0 196.0 360.0 0 Semiurban 0.0
536 LP002731 Female No 0 0 1.0 18165 0.0 125.0 360.0 1 Urban 1.0
537 LP002732 Male No 0 0 0.0 2550 2042.0 126.0 360.0 1 Rural 1.0
538 LP002734 Male Yes 0 1 0.0 6133 3906.0 324.0 360.0 1 Urban 1.0
539 LP002738 Male No 2 1 0.0 3617 0.0 107.0 360.0 1 Semiurban 1.0
540 LP002739 Male Yes 0 0 0.0 2917 536.0 66.0 360.0 1 Rural 0.0
541 LP002740 Male Yes 3+ 1 0.0 6417 0.0 157.0 180.0 1 Rural 1.0
542 LP002741 Female Yes 1 1 0.0 4608 2845.0 140.0 180.0 1 Semiurban 1.0
543 LP002743 Female No 0 1 0.0 2138 0.0 99.0 360.0 0 Semiurban 0.0
544 LP002753 Female No 1 1 0.0 3652 0.0 95.0 360.0 1 Semiurban 1.0
545 LP002755 Male Yes 1 0 0.0 2239 2524.0 128.0 360.0 1 Urban 1.0
546 LP002757 Female Yes 0 0 0.0 3017 663.0 102.0 360.0 0 Semiurban 1.0
547 LP002767 Male Yes 0 1 0.0 2768 1950.0 155.0 360.0 1 Rural 1.0
548 LP002768 Male No 0 0 0.0 3358 0.0 80.0 36.0 1 Semiurban 0.0
549 LP002772 Male No 0 1 0.0 2526 1783.0 145.0 360.0 1 Rural 1.0
550 LP002776 Female No 0 1 0.0 5000 0.0 103.0 360.0 0 Semiurban 0.0
551 LP002777 Male Yes 0 1 0.0 2785 2016.0 110.0 360.0 1 Rural 1.0
552 LP002778 Male Yes 2 1 1.0 6633 0.0 0.0 360.0 0 Rural 0.0
553 LP002784 Male Yes 1 0 0.0 2492 2375.0 0.0 360.0 1 Rural 1.0
554 LP002785 Male Yes 1 1 0.0 3333 3250.0 158.0 360.0 1 Urban 1.0
555 LP002788 Male Yes 0 0 0.0 2454 2333.0 181.0 360.0 0 Urban 0.0
556 LP002789 Male Yes 0 1 0.0 3593 4266.0 132.0 180.0 0 Rural 0.0
557 LP002792 Male Yes 1 1 0.0 5468 1032.0 26.0 360.0 1 Semiurban 1.0
558 LP002794 Female No 0 1 0.0 2667 1625.0 84.0 360.0 0 Urban 1.0
559 LP002795 Male Yes 3+ 1 1.0 10139 0.0 260.0 360.0 1 Semiurban 1.0
560 LP002798 Male Yes 0 1 0.0 3887 2669.0 162.0 360.0 1 Semiurban 1.0
561 LP002804 Female Yes 0 1 0.0 4180 2306.0 182.0 360.0 1 Semiurban 1.0
562 LP002807 Male Yes 2 0 0.0 3675 242.0 108.0 360.0 1 Semiurban 1.0
563 LP002813 Female Yes 1 1 1.0 19484 0.0 600.0 360.0 1 Semiurban 1.0
564 LP002820 Male Yes 0 1 0.0 5923 2054.0 211.0 360.0 1 Rural 1.0
565 LP002821 Male No 0 0 1.0 5800 0.0 132.0 360.0 1 Semiurban 1.0
566 LP002832 Male Yes 2 1 0.0 8799 0.0 258.0 360.0 0 Urban 0.0
567 LP002833 Male Yes 0 0 0.0 4467 0.0 120.0 360.0 0 Rural 1.0
568 LP002836 Male No 0 1 0.0 3333 0.0 70.0 360.0 1 Urban 1.0
569 LP002837 Male Yes 3+ 1 0.0 3400 2500.0 123.0 360.0 0 Rural 0.0
570 LP002840 Female No 0 1 0.0 2378 0.0 9.0 360.0 1 Urban 0.0
571 LP002841 Male Yes 0 1 0.0 3166 2064.0 104.0 360.0 0 Urban 0.0
572 LP002842 Male Yes 1 1 0.0 3417 1750.0 186.0 360.0 1 Urban 1.0
573 LP002847 Male Yes 1 0.0 5116 1451.0 165.0 360.0 0 Urban 0.0
574 LP002855 Male Yes 2 1 0.0 16666 0.0 275.0 360.0 1 Urban 1.0
575 LP002862 Male Yes 2 0 0.0 6125 1625.0 187.0 480.0 1 Semiurban 0.0
576 LP002863 Male Yes 3+ 1 0.0 6406 0.0 150.0 360.0 1 Semiurban 0.0
577 LP002868 Male Yes 2 1 0.0 3159 461.0 108.0 84.0 1 Urban 1.0
578 LP002872 Yes 0 1 0.0 3087 2210.0 136.0 360.0 0 Semiurban 0.0
579 LP002874 Male No 0 1 0.0 3229 2739.0 110.0 360.0 1 Urban 1.0
580 LP002877 Male Yes 1 1 0.0 1782 2232.0 107.0 360.0 1 Rural 1.0
581 LP002888 Male No 0 1 0.0 3182 2917.0 161.0 360.0 1 Urban 1.0
582 LP002892 Male Yes 2 1 0.0 6540 0.0 205.0 360.0 1 Semiurban 1.0
583 LP002893 Male No 0 1 0.0 1836 33837.0 90.0 360.0 1 Urban 0.0
584 LP002894 Female Yes 0 1 0.0 3166 0.0 36.0 360.0 1 Semiurban 1.0
585 LP002898 Male Yes 1 1 0.0 1880 0.0 61.0 360.0 0 Rural 0.0
586 LP002911 Male Yes 1 1 0.0 2787 1917.0 146.0 360.0 0 Rural 0.0
587 LP002912 Male Yes 1 1 0.0 4283 3000.0 172.0 84.0 1 Rural 0.0
588 LP002916 Male Yes 0 1 0.0 2297 1522.0 104.0 360.0 1 Urban 1.0
589 LP002917 Female No 0 0 0.0 2165 0.0 70.0 360.0 1 Semiurban 1.0
590 LP002925 No 0 1 0.0 4750 0.0 94.0 360.0 1 Semiurban 1.0
591 LP002926 Male Yes 2 1 1.0 2726 0.0 106.0 360.0 0 Semiurban 0.0
592 LP002928 Male Yes 0 1 0.0 3000 3416.0 56.0 180.0 1 Semiurban 1.0
593 LP002931 Male Yes 2 1 1.0 6000 0.0 205.0 240.0 1 Semiurban 0.0
594 LP002933 No 3+ 1 1.0 9357 0.0 292.0 360.0 1 Semiurban 1.0
595 LP002936 Male Yes 0 1 0.0 3859 3300.0 142.0 180.0 1 Rural 1.0
596 LP002938 Male Yes 0 1 1.0 16120 0.0 260.0 360.0 1 Urban 1.0
597 LP002940 Male No 0 0 0.0 3833 0.0 110.0 360.0 1 Rural 1.0
598 LP002941 Male Yes 2 0 1.0 6383 1000.0 187.0 360.0 1 Rural 0.0
599 LP002943 Male No 1 0.0 2987 0.0 88.0 360.0 0 Semiurban 0.0
600 LP002945 Male Yes 0 1 1.0 9963 0.0 180.0 360.0 1 Rural 1.0
601 LP002948 Male Yes 2 1 0.0 5780 0.0 192.0 360.0 1 Urban 1.0
602 LP002949 Female No 3+ 1 0.0 416 41667.0 350.0 180.0 0 Urban 0.0
603 LP002950 Male Yes 0 0 0.0 2894 2792.0 155.0 360.0 1 Rural 1.0
604 LP002953 Male Yes 3+ 1 0.0 5703 0.0 128.0 360.0 1 Urban 1.0
605 LP002958 Male No 0 1 0.0 3676 4301.0 172.0 360.0 1 Rural 1.0
606 LP002959 Female Yes 1 1 0.0 12000 0.0 496.0 360.0 1 Semiurban 1.0
607 LP002960 Male Yes 0 0 0.0 2400 3800.0 0.0 180.0 1 Urban 0.0
608 LP002961 Male Yes 1 1 0.0 3400 2500.0 173.0 360.0 1 Semiurban 1.0
609 LP002964 Male Yes 2 0 0.0 3987 1411.0 157.0 360.0 1 Rural 1.0
610 LP002974 Male Yes 0 1 0.0 3232 1950.0 108.0 360.0 1 Rural 1.0
611 LP002978 Female No 0 1 0.0 2900 0.0 71.0 360.0 1 Rural 1.0
612 LP002979 Male Yes 3+ 1 0.0 4106 0.0 40.0 180.0 1 Rural 1.0
613 LP002983 Male Yes 1 1 0.0 8072 240.0 253.0 360.0 1 Urban 1.0
614 LP002984 Male Yes 2 1 0.0 7583 0.0 187.0 360.0 1 Urban 1.0
615 LP002990 Female No 0 1 1.0 4583 0.0 133.0 360.0 0 Semiurban 0.0

BIN
abanin_daniil_lab_4/result.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 92 KiB

38
abanin_daniil_lab_5/README.md Normal file
View File

@ -0,0 +1,38 @@
## Лабораторная работа №5
### Ранжирование признаков
## ПИбд-41 Абанин Даниил
### Как запустить лабораторную работу:
* установить python, pandas, matplotlib, sklearn
* запустить проект (стартовая точка lab4)
### Какие технологии использовались:
* Язык программирования `Python`, библиотеки pandas, matplotlib, sklearn
* Среда разработки `PyCharm`
### Что делает лабораторная работа:
Программа решает задачу регрессии, используя полиномиальную регрессию.
Цель - предсказать сумму займа (LoanAmount), используя имеющиеся признаки: ApplicantIncome - доход заявителя, Credit_History - статус соответствия кредитной истории стандартам банка,
Education - наличие образования, Married - заявитель женат/замужем (Да/Нет), Self_Employed - самозанятый (Да/Нет)
### Тест
Зелёные маркеры на графике - тестовые результаты
Красные маркеры на графике - предсказанные результаты
При небольшом объёме тестовых данных, алгоритм показывает неплохие результаты обучения
![Result](grade_1.png)
![Result](result_1.png)
Но при увеличении объёма данных, алгоритм теряет свою эффективность
![Result](grade_2.png)
![Result](result_2.png)
Вывод: На малых объёмах данных алгоритм показывает свою эффективность. Но при большем объём стоит использовать другие методы для данного набора информации

BIN
abanin_daniil_lab_5/grade_1.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 13 KiB

BIN
abanin_daniil_lab_5/grade_2.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 10 KiB

33
abanin_daniil_lab_5/lab5.py Normal file
View File

@ -0,0 +1,33 @@
from matplotlib import pyplot as plt
from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import Pipeline
import pandas as pd
def start():
data = pd.read_csv('loan.csv')
x = data[['ApplicantIncome', 'Credit_History', 'Education', 'Married', 'Self_Employed']]
y = data[['LoanAmount']]
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=42)
poly = Pipeline([('poly', PolynomialFeatures(degree=3)),
('linear', LinearRegression())])
poly.fit(x_train, y_train)
y_predicted = poly.predict(x_test)
print('Оценка обучения:')
print(metrics.r2_score(y_test, y_predicted))
plt.figure(1, figsize=(16, 9))
plt.title('Сравнение результатов обучения')
plt.scatter(x=[i for i in range(len(x_test))], y=y_test, c='green', s=5)
plt.scatter(x=[i for i in range(len(x_test))], y=y_predicted, c='red', s=5)
plt.show()
start()

615
abanin_daniil_lab_5/loan.csv Normal file
View File

@ -0,0 +1,615 @@
Loan_ID,Gender,Married,Dependents,Education,Self_Employed,ApplicantIncome,CoapplicantIncome,LoanAmount,Loan_Amount_Term,Credit_History,Property_Area,Loan_Status
LP001002,Male,0.0,0,1,0.0,5849,0.0,360.0,1.0,0,Y,0.0
LP001003,Male,1.0,1,1,0.0,4583,1508.0,128.0,360.0,1,Rural,0.0
LP001005,Male,1.0,0,1,1.0,3000,0.0,66.0,360.0,1,Urban,1.0
LP001006,Male,1.0,0,0,0.0,2583,2358.0,120.0,360.0,1,Urban,1.0
LP001008,Male,0.0,0,1,0.0,6000,0.0,141.0,360.0,1,Urban,1.0
LP001011,Male,1.0,2,1,1.0,5417,4196.0,267.0,360.0,1,Urban,1.0
LP001013,Male,1.0,0,0,0.0,2333,1516.0,95.0,360.0,1,Urban,1.0
LP001014,Male,1.0,3+,1,0.0,3036,2504.0,158.0,360.0,0,Semiurban,0.0
LP001018,Male,1.0,2,1,0.0,4006,1526.0,168.0,360.0,1,Urban,1.0
LP001020,Male,1.0,1,1,0.0,12841,10968.0,349.0,360.0,1,Semiurban,0.0
LP001024,Male,1.0,2,1,0.0,3200,700.0,70.0,360.0,1,Urban,1.0
LP001027,Male,1.0,2,1,0.0,2500,1840.0,109.0,360.0,1,Urban,1.0
LP001028,Male,1.0,2,1,0.0,3073,8106.0,200.0,360.0,1,Urban,1.0
LP001029,Male,0.0,0,1,0.0,1853,2840.0,114.0,360.0,1,Rural,0.0
LP001030,Male,1.0,2,1,0.0,1299,1086.0,17.0,120.0,1,Urban,1.0
LP001032,Male,0.0,0,1,0.0,4950,0.0,125.0,360.0,1,Urban,1.0
LP001034,Male,0.0,1,0,0.0,3596,0.0,100.0,240.0,0,Urban,1.0
LP001036,Female,0.0,0,1,0.0,3510,0.0,76.0,360.0,0,Urban,0.0
LP001038,Male,1.0,0,0,0.0,4887,0.0,133.0,360.0,1,Rural,0.0
LP001041,Male,1.0,0,1,0.0,2600,3500.0,115.0,,1,Urban,1.0
LP001043,Male,1.0,0,0,0.0,7660,0.0,104.0,360.0,0,Urban,0.0
LP001046,Male,1.0,1,1,0.0,5955,5625.0,315.0,360.0,1,Urban,1.0
LP001047,Male,1.0,0,0,0.0,2600,1911.0,116.0,360.0,0,Semiurban,0.0
LP001050,,1.0,2,0,0.0,3365,1917.0,112.0,360.0,0,Rural,0.0
LP001052,Male,1.0,1,1,0.0,3717,2925.0,151.0,360.0,0,Semiurban,0.0
LP001066,Male,1.0,0,1,1.0,9560,0.0,191.0,360.0,1,Semiurban,1.0
LP001068,Male,1.0,0,1,0.0,2799,2253.0,122.0,360.0,1,Semiurban,1.0
LP001073,Male,1.0,2,0,0.0,4226,1040.0,110.0,360.0,1,Urban,1.0
LP001086,Male,0.0,0,0,0.0,1442,0.0,35.0,360.0,1,Urban,0.0
LP001087,Female,0.0,2,1,0.0,3750,2083.0,120.0,360.0,1,Semiurban,1.0
LP001091,Male,1.0,1,1,0.0,4166,3369.0,201.0,360.0,0,Urban,0.0
LP001095,Male,0.0,0,1,0.0,3167,0.0,74.0,360.0,1,Urban,0.0
LP001097,Male,0.0,1,1,1.0,4692,0.0,106.0,360.0,1,Rural,0.0
LP001098,Male,1.0,0,1,0.0,3500,1667.0,114.0,360.0,1,Semiurban,1.0
LP001100,Male,0.0,3+,1,0.0,12500,3000.0,320.0,360.0,1,Rural,0.0
LP001106,Male,1.0,0,1,0.0,2275,2067.0,0.0,360.0,1,Urban,1.0
LP001109,Male,1.0,0,1,0.0,1828,1330.0,100.0,,0,Urban,0.0
LP001112,Female,1.0,0,1,0.0,3667,1459.0,144.0,360.0,1,Semiurban,1.0
LP001114,Male,0.0,0,1,0.0,4166,7210.0,184.0,360.0,1,Urban,1.0
LP001116,Male,0.0,0,0,0.0,3748,1668.0,110.0,360.0,1,Semiurban,1.0
LP001119,Male,0.0,0,1,0.0,3600,0.0,80.0,360.0,1,Urban,0.0
LP001120,Male,0.0,0,1,0.0,1800,1213.0,47.0,360.0,1,Urban,1.0
LP001123,Male,1.0,0,1,0.0,2400,0.0,75.0,360.0,0,Urban,1.0
LP001131,Male,1.0,0,1,0.0,3941,2336.0,134.0,360.0,1,Semiurban,1.0
LP001136,Male,1.0,0,0,1.0,4695,0.0,96.0,,1,Urban,1.0
LP001137,Female,0.0,0,1,0.0,3410,0.0,88.0,,1,Urban,1.0
LP001138,Male,1.0,1,1,0.0,5649,0.0,44.0,360.0,1,Urban,1.0
LP001144,Male,1.0,0,1,0.0,5821,0.0,144.0,360.0,1,Urban,1.0
LP001146,Female,1.0,0,1,0.0,2645,3440.0,120.0,360.0,0,Urban,0.0
LP001151,Female,0.0,0,1,0.0,4000,2275.0,144.0,360.0,1,Semiurban,1.0
LP001155,Female,1.0,0,0,0.0,1928,1644.0,100.0,360.0,1,Semiurban,1.0
LP001157,Female,0.0,0,1,0.0,3086,0.0,120.0,360.0,1,Semiurban,1.0
LP001164,Female,0.0,0,1,0.0,4230,0.0,112.0,360.0,1,Semiurban,0.0
LP001179,Male,1.0,2,1,0.0,4616,0.0,134.0,360.0,1,Urban,0.0
LP001186,Female,1.0,1,1,1.0,11500,0.0,286.0,360.0,0,Urban,0.0
LP001194,Male,1.0,2,1,0.0,2708,1167.0,97.0,360.0,1,Semiurban,1.0
LP001195,Male,1.0,0,1,0.0,2132,1591.0,96.0,360.0,1,Semiurban,1.0
LP001197,Male,1.0,0,1,0.0,3366,2200.0,135.0,360.0,1,Rural,0.0
LP001198,Male,1.0,1,1,0.0,8080,2250.0,180.0,360.0,1,Urban,1.0
LP001199,Male,1.0,2,0,0.0,3357,2859.0,144.0,360.0,1,Urban,1.0
LP001205,Male,1.0,0,1,0.0,2500,3796.0,120.0,360.0,1,Urban,1.0
LP001206,Male,1.0,3+,1,0.0,3029,0.0,99.0,360.0,1,Urban,1.0
LP001207,Male,1.0,0,0,1.0,2609,3449.0,165.0,180.0,0,Rural,0.0
LP001213,Male,1.0,1,1,0.0,4945,0.0,0.0,360.0,0,Rural,0.0
LP001222,Female,0.0,0,1,0.0,4166,0.0,116.0,360.0,0,Semiurban,0.0
LP001225,Male,1.0,0,1,0.0,5726,4595.0,258.0,360.0,1,Semiurban,0.0
LP001228,Male,0.0,0,0,0.0,3200,2254.0,126.0,180.0,0,Urban,0.0
LP001233,Male,1.0,1,1,0.0,10750,0.0,312.0,360.0,1,Urban,1.0
LP001238,Male,1.0,3+,0,1.0,7100,0.0,125.0,60.0,1,Urban,1.0
LP001241,Female,0.0,0,1,0.0,4300,0.0,136.0,360.0,0,Semiurban,0.0
LP001243,Male,1.0,0,1,0.0,3208,3066.0,172.0,360.0,1,Urban,1.0
LP001245,Male,1.0,2,0,1.0,1875,1875.0,97.0,360.0,1,Semiurban,1.0
LP001248,Male,0.0,0,1,0.0,3500,0.0,81.0,300.0,1,Semiurban,1.0
LP001250,Male,1.0,3+,0,0.0,4755,0.0,95.0,,0,Semiurban,0.0
LP001253,Male,1.0,3+,1,1.0,5266,1774.0,187.0,360.0,1,Semiurban,1.0
LP001255,Male,0.0,0,1,0.0,3750,0.0,113.0,480.0,1,Urban,0.0
LP001256,Male,0.0,0,1,0.0,3750,4750.0,176.0,360.0,1,Urban,0.0
LP001259,Male,1.0,1,1,1.0,1000,3022.0,110.0,360.0,1,Urban,0.0
LP001263,Male,1.0,3+,1,0.0,3167,4000.0,180.0,300.0,0,Semiurban,0.0
LP001264,Male,1.0,3+,0,1.0,3333,2166.0,130.0,360.0,0,Semiurban,1.0
LP001265,Female,0.0,0,1,0.0,3846,0.0,111.0,360.0,1,Semiurban,1.0
LP001266,Male,1.0,1,1,1.0,2395,0.0,0.0,360.0,1,Semiurban,1.0
LP001267,Female,1.0,2,1,0.0,1378,1881.0,167.0,360.0,1,Urban,0.0
LP001273,Male,1.0,0,1,0.0,6000,2250.0,265.0,360.0,0,Semiurban,0.0
LP001275,Male,1.0,1,1,0.0,3988,0.0,50.0,240.0,1,Urban,1.0
LP001279,Male,0.0,0,1,0.0,2366,2531.0,136.0,360.0,1,Semiurban,1.0
LP001280,Male,1.0,2,0,0.0,3333,2000.0,99.0,360.0,0,Semiurban,1.0
LP001282,Male,1.0,0,1,0.0,2500,2118.0,104.0,360.0,1,Semiurban,1.0
LP001289,Male,0.0,0,1,0.0,8566,0.0,210.0,360.0,1,Urban,1.0
LP001310,Male,1.0,0,1,0.0,5695,4167.0,175.0,360.0,1,Semiurban,1.0
LP001316,Male,1.0,0,1,0.0,2958,2900.0,131.0,360.0,1,Semiurban,1.0
LP001318,Male,1.0,2,1,0.0,6250,5654.0,188.0,180.0,1,Semiurban,1.0
LP001319,Male,1.0,2,0,0.0,3273,1820.0,81.0,360.0,1,Urban,1.0
LP001322,Male,0.0,0,1,0.0,4133,0.0,122.0,360.0,1,Semiurban,1.0
LP001325,Male,0.0,0,0,0.0,3620,0.0,25.0,120.0,1,Semiurban,1.0
LP001326,Male,0.0,0,1,0.0,6782,0.0,0.0,360.0,0,Urban,0.0
LP001327,Female,1.0,0,1,0.0,2484,2302.0,137.0,360.0,1,Semiurban,1.0
LP001333,Male,1.0,0,1,0.0,1977,997.0,50.0,360.0,1,Semiurban,1.0
LP001334,Male,1.0,0,0,0.0,4188,0.0,115.0,180.0,1,Semiurban,1.0
LP001343,Male,1.0,0,1,0.0,1759,3541.0,131.0,360.0,1,Semiurban,1.0
LP001345,Male,1.0,2,0,0.0,4288,3263.0,133.0,180.0,1,Urban,1.0
LP001349,Male,0.0,0,1,0.0,4843,3806.0,151.0,360.0,1,Semiurban,1.0
LP001350,Male,1.0,,1,0.0,13650,0.0,0.0,360.0,1,Urban,1.0
LP001356,Male,1.0,0,1,0.0,4652,3583.0,0.0,360.0,1,Semiurban,1.0
LP001357,Male,0.0,,1,0.0,3816,754.0,160.0,360.0,1,Urban,1.0
LP001367,Male,1.0,1,1,0.0,3052,1030.0,100.0,360.0,1,Urban,1.0
LP001369,Male,1.0,2,1,0.0,11417,1126.0,225.0,360.0,1,Urban,1.0
LP001370,Male,0.0,0,0,0.0,7333,0.0,120.0,360.0,1,Rural,0.0
LP001379,Male,1.0,2,1,0.0,3800,3600.0,216.0,360.0,0,Urban,0.0
LP001384,Male,1.0,3+,0,0.0,2071,754.0,94.0,480.0,1,Semiurban,1.0
LP001385,Male,0.0,0,1,0.0,5316,0.0,136.0,360.0,1,Urban,1.0
LP001387,Female,1.0,0,1,0.0,2929,2333.0,139.0,360.0,1,Semiurban,1.0
LP001391,Male,1.0,0,0,0.0,3572,4114.0,152.0,,0,Rural,0.0
LP001392,Female,0.0,1,1,1.0,7451,0.0,0.0,360.0,1,Semiurban,1.0
LP001398,Male,0.0,0,1,0.0,5050,0.0,118.0,360.0,1,Semiurban,1.0
LP001401,Male,1.0,1,1,0.0,14583,0.0,185.0,180.0,1,Rural,1.0
LP001404,Female,1.0,0,1,0.0,3167,2283.0,154.0,360.0,1,Semiurban,1.0
LP001405,Male,1.0,1,1,0.0,2214,1398.0,85.0,360.0,0,Urban,1.0
LP001421,Male,1.0,0,1,0.0,5568,2142.0,175.0,360.0,1,Rural,0.0
LP001422,Female,0.0,0,1,0.0,10408,0.0,259.0,360.0,1,Urban,1.0
LP001426,Male,1.0,,1,0.0,5667,2667.0,180.0,360.0,1,Rural,1.0
LP001430,Female,0.0,0,1,0.0,4166,0.0,44.0,360.0,1,Semiurban,1.0
LP001431,Female,0.0,0,1,0.0,2137,8980.0,137.0,360.0,0,Semiurban,1.0
LP001432,Male,1.0,2,1,0.0,2957,0.0,81.0,360.0,1,Semiurban,1.0
LP001439,Male,1.0,0,0,0.0,4300,2014.0,194.0,360.0,1,Rural,1.0
LP001443,Female,0.0,0,1,0.0,3692,0.0,93.0,360.0,0,Rural,1.0
LP001448,,1.0,3+,1,0.0,23803,0.0,370.0,360.0,1,Rural,1.0
LP001449,Male,0.0,0,1,0.0,3865,1640.0,0.0,360.0,1,Rural,1.0
LP001451,Male,1.0,1,1,1.0,10513,3850.0,160.0,180.0,0,Urban,0.0
LP001465,Male,1.0,0,1,0.0,6080,2569.0,182.0,360.0,0,Rural,0.0
LP001469,Male,0.0,0,1,1.0,20166,0.0,650.0,480.0,0,Urban,1.0
LP001473,Male,0.0,0,1,0.0,2014,1929.0,74.0,360.0,1,Urban,1.0
LP001478,Male,0.0,0,1,0.0,2718,0.0,70.0,360.0,1,Semiurban,1.0
LP001482,Male,1.0,0,1,1.0,3459,0.0,25.0,120.0,1,Semiurban,1.0
LP001487,Male,0.0,0,1,0.0,4895,0.0,102.0,360.0,1,Semiurban,1.0
LP001488,Male,1.0,3+,1,0.0,4000,7750.0,290.0,360.0,1,Semiurban,0.0
LP001489,Female,1.0,0,1,0.0,4583,0.0,84.0,360.0,1,Rural,0.0
LP001491,Male,1.0,2,1,1.0,3316,3500.0,88.0,360.0,1,Urban,1.0
LP001492,Male,0.0,0,1,0.0,14999,0.0,242.0,360.0,0,Semiurban,0.0
LP001493,Male,1.0,2,0,0.0,4200,1430.0,129.0,360.0,1,Rural,0.0
LP001497,Male,1.0,2,1,0.0,5042,2083.0,185.0,360.0,1,Rural,0.0
LP001498,Male,0.0,0,1,0.0,5417,0.0,168.0,360.0,1,Urban,1.0
LP001504,Male,0.0,0,1,1.0,6950,0.0,175.0,180.0,1,Semiurban,1.0
LP001507,Male,1.0,0,1,0.0,2698,2034.0,122.0,360.0,1,Semiurban,1.0
LP001508,Male,1.0,2,1,0.0,11757,0.0,187.0,180.0,1,Urban,1.0
LP001514,Female,1.0,0,1,0.0,2330,4486.0,100.0,360.0,1,Semiurban,1.0
LP001516,Female,1.0,2,1,0.0,14866,0.0,70.0,360.0,1,Urban,1.0
LP001518,Male,1.0,1,1,0.0,1538,1425.0,30.0,360.0,1,Urban,1.0
LP001519,Female,0.0,0,1,0.0,10000,1666.0,225.0,360.0,1,Rural,0.0
LP001520,Male,1.0,0,1,0.0,4860,830.0,125.0,360.0,1,Semiurban,1.0
LP001528,Male,0.0,0,1,0.0,6277,0.0,118.0,360.0,0,Rural,0.0
LP001529,Male,1.0,0,1,1.0,2577,3750.0,152.0,360.0,1,Rural,1.0
LP001531,Male,0.0,0,1,0.0,9166,0.0,244.0,360.0,1,Urban,0.0
LP001532,Male,1.0,2,0,0.0,2281,0.0,113.0,360.0,1,Rural,0.0
LP001535,Male,0.0,0,1,0.0,3254,0.0,50.0,360.0,1,Urban,1.0
LP001536,Male,1.0,3+,1,0.0,39999,0.0,600.0,180.0,0,Semiurban,1.0
LP001541,Male,1.0,1,1,0.0,6000,0.0,160.0,360.0,0,Rural,1.0
LP001543,Male,1.0,1,1,0.0,9538,0.0,187.0,360.0,1,Urban,1.0
LP001546,Male,0.0,0,1,0.0,2980,2083.0,120.0,360.0,1,Rural,1.0
LP001552,Male,1.0,0,1,0.0,4583,5625.0,255.0,360.0,1,Semiurban,1.0
LP001560,Male,1.0,0,0,0.0,1863,1041.0,98.0,360.0,1,Semiurban,1.0
LP001562,Male,1.0,0,1,0.0,7933,0.0,275.0,360.0,1,Urban,0.0
LP001565,Male,1.0,1,1,0.0,3089,1280.0,121.0,360.0,0,Semiurban,0.0
LP001570,Male,1.0,2,1,0.0,4167,1447.0,158.0,360.0,1,Rural,1.0
LP001572,Male,1.0,0,1,0.0,9323,0.0,75.0,180.0,1,Urban,1.0
LP001574,Male,1.0,0,1,0.0,3707,3166.0,182.0,,1,Rural,1.0
LP001577,Female,1.0,0,1,0.0,4583,0.0,112.0,360.0,1,Rural,0.0
LP001578,Male,1.0,0,1,0.0,2439,3333.0,129.0,360.0,1,Rural,1.0
LP001579,Male,0.0,0,1,0.0,2237,0.0,63.0,480.0,0,Semiurban,0.0
LP001580,Male,1.0,2,1,0.0,8000,0.0,200.0,360.0,1,Semiurban,1.0
LP001581,Male,1.0,0,0,0.0,1820,1769.0,95.0,360.0,1,Rural,1.0
LP001585,,1.0,3+,1,0.0,51763,0.0,700.0,300.0,1,Urban,1.0
LP001586,Male,1.0,3+,0,0.0,3522,0.0,81.0,180.0,1,Rural,0.0
LP001594,Male,1.0,0,1,0.0,5708,5625.0,187.0,360.0,1,Semiurban,1.0
LP001603,Male,1.0,0,0,1.0,4344,736.0,87.0,360.0,1,Semiurban,0.0
LP001606,Male,1.0,0,1,0.0,3497,1964.0,116.0,360.0,1,Rural,1.0
LP001608,Male,1.0,2,1,0.0,2045,1619.0,101.0,360.0,1,Rural,1.0
LP001610,Male,1.0,3+,1,0.0,5516,11300.0,495.0,360.0,0,Semiurban,0.0
LP001616,Male,1.0,1,1,0.0,3750,0.0,116.0,360.0,1,Semiurban,1.0
LP001630,Male,0.0,0,0,0.0,2333,1451.0,102.0,480.0,0,Urban,0.0
LP001633,Male,1.0,1,1,0.0,6400,7250.0,180.0,360.0,0,Urban,0.0
LP001634,Male,0.0,0,1,0.0,1916,5063.0,67.0,360.0,0,Rural,0.0
LP001636,Male,1.0,0,1,0.0,4600,0.0,73.0,180.0,1,Semiurban,1.0
LP001637,Male,1.0,1,1,0.0,33846,0.0,260.0,360.0,1,Semiurban,0.0
LP001639,Female,1.0,0,1,0.0,3625,0.0,108.0,360.0,1,Semiurban,1.0
LP001640,Male,1.0,0,1,1.0,39147,4750.0,120.0,360.0,1,Semiurban,1.0
LP001641,Male,1.0,1,1,1.0,2178,0.0,66.0,300.0,0,Rural,0.0
LP001643,Male,1.0,0,1,0.0,2383,2138.0,58.0,360.0,0,Rural,1.0
LP001644,,1.0,0,1,1.0,674,5296.0,168.0,360.0,1,Rural,1.0
LP001647,Male,1.0,0,1,0.0,9328,0.0,188.0,180.0,1,Rural,1.0
LP001653,Male,0.0,0,0,0.0,4885,0.0,48.0,360.0,1,Rural,1.0
LP001656,Male,0.0,0,1,0.0,12000,0.0,164.0,360.0,1,Semiurban,0.0
LP001657,Male,1.0,0,0,0.0,6033,0.0,160.0,360.0,1,Urban,0.0
LP001658,Male,0.0,0,1,0.0,3858,0.0,76.0,360.0,1,Semiurban,1.0
LP001664,Male,0.0,0,1,0.0,4191,0.0,120.0,360.0,1,Rural,1.0
LP001665,Male,1.0,1,1,0.0,3125,2583.0,170.0,360.0,1,Semiurban,0.0
LP001666,Male,0.0,0,1,0.0,8333,3750.0,187.0,360.0,1,Rural,1.0
LP001669,Female,0.0,0,0,0.0,1907,2365.0,120.0,,1,Urban,1.0
LP001671,Female,1.0,0,1,0.0,3416,2816.0,113.0,360.0,0,Semiurban,1.0
LP001673,Male,0.0,0,1,1.0,11000,0.0,83.0,360.0,1,Urban,0.0
LP001674,Male,1.0,1,0,0.0,2600,2500.0,90.0,360.0,1,Semiurban,1.0
LP001677,Male,0.0,2,1,0.0,4923,0.0,166.0,360.0,0,Semiurban,1.0
LP001682,Male,1.0,3+,0,0.0,3992,0.0,0.0,180.0,1,Urban,0.0
LP001688,Male,1.0,1,0,0.0,3500,1083.0,135.0,360.0,1,Urban,1.0
LP001691,Male,1.0,2,0,0.0,3917,0.0,124.0,360.0,1,Semiurban,1.0
LP001692,Female,0.0,0,0,0.0,4408,0.0,120.0,360.0,1,Semiurban,1.0
LP001693,Female,0.0,0,1,0.0,3244,0.0,80.0,360.0,1,Urban,1.0
LP001698,Male,0.0,0,0,0.0,3975,2531.0,55.0,360.0,1,Rural,1.0
LP001699,Male,0.0,0,1,0.0,2479,0.0,59.0,360.0,1,Urban,1.0
LP001702,Male,0.0,0,1,0.0,3418,0.0,127.0,360.0,1,Semiurban,0.0
LP001708,Female,0.0,0,1,0.0,10000,0.0,214.0,360.0,1,Semiurban,0.0
LP001711,Male,1.0,3+,1,0.0,3430,1250.0,128.0,360.0,0,Semiurban,0.0
LP001713,Male,1.0,1,1,1.0,7787,0.0,240.0,360.0,1,Urban,1.0
LP001715,Male,1.0,3+,0,1.0,5703,0.0,130.0,360.0,1,Rural,1.0
LP001716,Male,1.0,0,1,0.0,3173,3021.0,137.0,360.0,1,Urban,1.0
LP001720,Male,1.0,3+,0,0.0,3850,983.0,100.0,360.0,1,Semiurban,1.0
LP001722,Male,1.0,0,1,0.0,150,1800.0,135.0,360.0,1,Rural,0.0
LP001726,Male,1.0,0,1,0.0,3727,1775.0,131.0,360.0,1,Semiurban,1.0
LP001732,Male,1.0,2,1,0.0,5000,0.0,72.0,360.0,0,Semiurban,0.0
LP001734,Female,1.0,2,1,0.0,4283,2383.0,127.0,360.0,0,Semiurban,1.0
LP001736,Male,1.0,0,1,0.0,2221,0.0,60.0,360.0,0,Urban,0.0
LP001743,Male,1.0,2,1,0.0,4009,1717.0,116.0,360.0,1,Semiurban,1.0
LP001744,Male,0.0,0,1,0.0,2971,2791.0,144.0,360.0,1,Semiurban,1.0
LP001749,Male,1.0,0,1,0.0,7578,1010.0,175.0,,1,Semiurban,1.0
LP001750,Male,1.0,0,1,0.0,6250,0.0,128.0,360.0,1,Semiurban,1.0
LP001751,Male,1.0,0,1,0.0,3250,0.0,170.0,360.0,1,Rural,0.0
LP001754,Male,1.0,,0,1.0,4735,0.0,138.0,360.0,1,Urban,0.0
LP001758,Male,1.0,2,1,0.0,6250,1695.0,210.0,360.0,1,Semiurban,1.0
LP001760,Male,0.0,,1,0.0,4758,0.0,158.0,480.0,1,Semiurban,1.0
LP001761,Male,0.0,0,1,1.0,6400,0.0,200.0,360.0,1,Rural,1.0
LP001765,Male,1.0,1,1,0.0,2491,2054.0,104.0,360.0,1,Semiurban,1.0
LP001768,Male,1.0,0,1,0.0,3716,0.0,42.0,180.0,1,Rural,1.0
LP001770,Male,0.0,0,0,0.0,3189,2598.0,120.0,,1,Rural,1.0
LP001776,Female,0.0,0,1,0.0,8333,0.0,280.0,360.0,1,Semiurban,1.0
LP001778,Male,1.0,1,1,0.0,3155,1779.0,140.0,360.0,1,Semiurban,1.0
LP001784,Male,1.0,1,1,0.0,5500,1260.0,170.0,360.0,1,Rural,1.0
LP001786,Male,1.0,0,1,0.0,5746,0.0,255.0,360.0,0,Urban,0.0
LP001788,Female,0.0,0,1,1.0,3463,0.0,122.0,360.0,0,Urban,1.0
LP001790,Female,0.0,1,1,0.0,3812,0.0,112.0,360.0,1,Rural,1.0
LP001792,Male,1.0,1,1,0.0,3315,0.0,96.0,360.0,1,Semiurban,1.0
LP001798,Male,1.0,2,1,0.0,5819,5000.0,120.0,360.0,1,Rural,1.0
LP001800,Male,1.0,1,0,0.0,2510,1983.0,140.0,180.0,1,Urban,0.0
LP001806,Male,0.0,0,1,0.0,2965,5701.0,155.0,60.0,1,Urban,1.0
LP001807,Male,1.0,2,1,1.0,6250,1300.0,108.0,360.0,1,Rural,1.0
LP001811,Male,1.0,0,0,0.0,3406,4417.0,123.0,360.0,1,Semiurban,1.0
LP001813,Male,0.0,0,1,1.0,6050,4333.0,120.0,180.0,1,Urban,0.0
LP001814,Male,1.0,2,1,0.0,9703,0.0,112.0,360.0,1,Urban,1.0
LP001819,Male,1.0,1,0,0.0,6608,0.0,137.0,180.0,1,Urban,1.0
LP001824,Male,1.0,1,1,0.0,2882,1843.0,123.0,480.0,1,Semiurban,1.0
LP001825,Male,1.0,0,1,0.0,1809,1868.0,90.0,360.0,1,Urban,1.0
LP001835,Male,1.0,0,0,0.0,1668,3890.0,201.0,360.0,0,Semiurban,0.0
LP001836,Female,0.0,2,1,0.0,3427,0.0,138.0,360.0,1,Urban,0.0
LP001841,Male,0.0,0,0,1.0,2583,2167.0,104.0,360.0,1,Rural,1.0
LP001843,Male,1.0,1,0,0.0,2661,7101.0,279.0,180.0,1,Semiurban,1.0
LP001844,Male,0.0,0,1,1.0,16250,0.0,192.0,360.0,0,Urban,0.0
LP001846,Female,0.0,3+,1,0.0,3083,0.0,255.0,360.0,1,Rural,1.0
LP001849,Male,0.0,0,0,0.0,6045,0.0,115.0,360.0,0,Rural,0.0
LP001854,Male,1.0,3+,1,0.0,5250,0.0,94.0,360.0,1,Urban,0.0
LP001859,Male,1.0,0,1,0.0,14683,2100.0,304.0,360.0,1,Rural,0.0
LP001864,Male,1.0,3+,0,0.0,4931,0.0,128.0,360.0,0,Semiurban,0.0
LP001865,Male,1.0,1,1,0.0,6083,4250.0,330.0,360.0,0,Urban,1.0
LP001868,Male,0.0,0,1,0.0,2060,2209.0,134.0,360.0,1,Semiurban,1.0
LP001870,Female,0.0,1,1,0.0,3481,0.0,155.0,36.0,1,Semiurban,0.0
LP001871,Female,0.0,0,1,0.0,7200,0.0,120.0,360.0,1,Rural,1.0
LP001872,Male,0.0,0,1,1.0,5166,0.0,128.0,360.0,1,Semiurban,1.0
LP001875,Male,0.0,0,1,0.0,4095,3447.0,151.0,360.0,1,Rural,1.0
LP001877,Male,1.0,2,1,0.0,4708,1387.0,150.0,360.0,1,Semiurban,1.0
LP001882,Male,1.0,3+,1,0.0,4333,1811.0,160.0,360.0,0,Urban,1.0
LP001883,Female,0.0,0,1,0.0,3418,0.0,135.0,360.0,1,Rural,0.0
LP001884,Female,0.0,1,1,0.0,2876,1560.0,90.0,360.0,1,Urban,1.0
LP001888,Female,0.0,0,1,0.0,3237,0.0,30.0,360.0,1,Urban,1.0
LP001891,Male,1.0,0,1,0.0,11146,0.0,136.0,360.0,1,Urban,1.0
LP001892,Male,0.0,0,1,0.0,2833,1857.0,126.0,360.0,1,Rural,1.0
LP001894,Male,1.0,0,1,0.0,2620,2223.0,150.0,360.0,1,Semiurban,1.0
LP001896,Male,1.0,2,1,0.0,3900,0.0,90.0,360.0,1,Semiurban,1.0
LP001900,Male,1.0,1,1,0.0,2750,1842.0,115.0,360.0,1,Semiurban,1.0
LP001903,Male,1.0,0,1,0.0,3993,3274.0,207.0,360.0,1,Semiurban,1.0
LP001904,Male,1.0,0,1,0.0,3103,1300.0,80.0,360.0,1,Urban,1.0
LP001907,Male,1.0,0,1,0.0,14583,0.0,436.0,360.0,1,Semiurban,1.0
LP001908,Female,1.0,0,0,0.0,4100,0.0,124.0,360.0,0,Rural,1.0
LP001910,Male,0.0,1,0,1.0,4053,2426.0,158.0,360.0,0,Urban,0.0
LP001914,Male,1.0,0,1,0.0,3927,800.0,112.0,360.0,1,Semiurban,1.0
LP001915,Male,1.0,2,1,0.0,2301,985.7999878,78.0,180.0,1,Urban,1.0
LP001917,Female,0.0,0,1,0.0,1811,1666.0,54.0,360.0,1,Urban,1.0
LP001922,Male,1.0,0,1,0.0,20667,0.0,0.0,360.0,1,Rural,0.0
LP001924,Male,0.0,0,1,0.0,3158,3053.0,89.0,360.0,1,Rural,1.0
LP001925,Female,0.0,0,1,1.0,2600,1717.0,99.0,300.0,1,Semiurban,0.0
LP001926,Male,1.0,0,1,0.0,3704,2000.0,120.0,360.0,1,Rural,1.0
LP001931,Female,0.0,0,1,0.0,4124,0.0,115.0,360.0,1,Semiurban,1.0
LP001935,Male,0.0,0,1,0.0,9508,0.0,187.0,360.0,1,Rural,1.0
LP001936,Male,1.0,0,1,0.0,3075,2416.0,139.0,360.0,1,Rural,1.0
LP001938,Male,1.0,2,1,0.0,4400,0.0,127.0,360.0,0,Semiurban,0.0
LP001940,Male,1.0,2,1,0.0,3153,1560.0,134.0,360.0,1,Urban,1.0
LP001945,Female,0.0,,1,0.0,5417,0.0,143.0,480.0,0,Urban,0.0
LP001947,Male,1.0,0,1,0.0,2383,3334.0,172.0,360.0,1,Semiurban,1.0
LP001949,Male,1.0,3+,1,0.0,4416,1250.0,110.0,360.0,1,Urban,1.0
LP001953,Male,1.0,1,1,0.0,6875,0.0,200.0,360.0,1,Semiurban,1.0
LP001954,Female,1.0,1,1,0.0,4666,0.0,135.0,360.0,1,Urban,1.0
LP001955,Female,0.0,0,1,0.0,5000,2541.0,151.0,480.0,1,Rural,0.0
LP001963,Male,1.0,1,1,0.0,2014,2925.0,113.0,360.0,1,Urban,0.0
LP001964,Male,1.0,0,0,0.0,1800,2934.0,93.0,360.0,0,Urban,0.0
LP001972,Male,1.0,,0,0.0,2875,1750.0,105.0,360.0,1,Semiurban,1.0
LP001974,Female,0.0,0,1,0.0,5000,0.0,132.0,360.0,1,Rural,1.0
LP001977,Male,1.0,1,1,0.0,1625,1803.0,96.0,360.0,1,Urban,1.0
LP001978,Male,0.0,0,1,0.0,4000,2500.0,140.0,360.0,1,Rural,1.0
LP001990,Male,0.0,0,0,0.0,2000,0.0,0.0,360.0,1,Urban,0.0
LP001993,Female,0.0,0,1,0.0,3762,1666.0,135.0,360.0,1,Rural,1.0
LP001994,Female,0.0,0,1,0.0,2400,1863.0,104.0,360.0,0,Urban,0.0
LP001996,Male,0.0,0,1,0.0,20233,0.0,480.0,360.0,1,Rural,0.0
LP001998,Male,1.0,2,0,0.0,7667,0.0,185.0,360.0,0,Rural,1.0
LP002002,Female,0.0,0,1,0.0,2917,0.0,84.0,360.0,1,Semiurban,1.0
LP002004,Male,0.0,0,0,0.0,2927,2405.0,111.0,360.0,1,Semiurban,1.0
LP002006,Female,0.0,0,1,0.0,2507,0.0,56.0,360.0,1,Rural,1.0
LP002008,Male,1.0,2,1,1.0,5746,0.0,144.0,84.0,0,Rural,1.0
LP002024,,1.0,0,1,0.0,2473,1843.0,159.0,360.0,1,Rural,0.0
LP002031,Male,1.0,1,0,0.0,3399,1640.0,111.0,180.0,1,Urban,1.0
LP002035,Male,1.0,2,1,0.0,3717,0.0,120.0,360.0,1,Semiurban,1.0
LP002036,Male,1.0,0,1,0.0,2058,2134.0,88.0,360.0,0,Urban,1.0
LP002043,Female,0.0,1,1,0.0,3541,0.0,112.0,360.0,0,Semiurban,1.0
LP002050,Male,1.0,1,1,1.0,10000,0.0,155.0,360.0,1,Rural,0.0
LP002051,Male,1.0,0,1,0.0,2400,2167.0,115.0,360.0,1,Semiurban,1.0
LP002053,Male,1.0,3+,1,0.0,4342,189.0,124.0,360.0,1,Semiurban,1.0
LP002054,Male,1.0,2,0,0.0,3601,1590.0,0.0,360.0,1,Rural,1.0
LP002055,Female,0.0,0,1,0.0,3166,2985.0,132.0,360.0,0,Rural,1.0
LP002065,Male,1.0,3+,1,0.0,15000,0.0,300.0,360.0,1,Rural,1.0
LP002067,Male,1.0,1,1,1.0,8666,4983.0,376.0,360.0,0,Rural,0.0
LP002068,Male,0.0,0,1,0.0,4917,0.0,130.0,360.0,0,Rural,1.0
LP002082,Male,1.0,0,1,1.0,5818,2160.0,184.0,360.0,1,Semiurban,1.0
LP002086,Female,1.0,0,1,0.0,4333,2451.0,110.0,360.0,1,Urban,0.0
LP002087,Female,0.0,0,1,0.0,2500,0.0,67.0,360.0,1,Urban,1.0
LP002097,Male,0.0,1,1,0.0,4384,1793.0,117.0,360.0,1,Urban,1.0
LP002098,Male,0.0,0,1,0.0,2935,0.0,98.0,360.0,1,Semiurban,1.0
LP002100,Male,0.0,,1,0.0,2833,0.0,71.0,360.0,1,Urban,1.0
LP002101,Male,1.0,0,1,0.0,63337,0.0,490.0,180.0,1,Urban,1.0
LP002103,,1.0,1,1,1.0,9833,1833.0,182.0,180.0,1,Urban,1.0
LP002106,Male,1.0,,1,1.0,5503,4490.0,70.0,,1,Semiurban,1.0
LP002110,Male,1.0,1,1,0.0,5250,688.0,160.0,360.0,1,Rural,1.0
LP002112,Male,1.0,2,1,1.0,2500,4600.0,176.0,360.0,1,Rural,1.0
LP002113,Female,0.0,3+,0,0.0,1830,0.0,0.0,360.0,0,Urban,0.0
LP002114,Female,0.0,0,1,0.0,4160,0.0,71.0,360.0,1,Semiurban,1.0
LP002115,Male,1.0,3+,0,0.0,2647,1587.0,173.0,360.0,1,Rural,0.0
LP002116,Female,0.0,0,1,0.0,2378,0.0,46.0,360.0,1,Rural,0.0
LP002119,Male,1.0,1,0,0.0,4554,1229.0,158.0,360.0,1,Urban,1.0
LP002126,Male,1.0,3+,0,0.0,3173,0.0,74.0,360.0,1,Semiurban,1.0
LP002128,Male,1.0,2,1,0.0,2583,2330.0,125.0,360.0,1,Rural,1.0
LP002129,Male,1.0,0,1,0.0,2499,2458.0,160.0,360.0,1,Semiurban,1.0
LP002130,Male,1.0,,0,0.0,3523,3230.0,152.0,360.0,0,Rural,0.0
LP002131,Male,1.0,2,0,0.0,3083,2168.0,126.0,360.0,1,Urban,1.0
LP002137,Male,1.0,0,1,0.0,6333,4583.0,259.0,360.0,0,Semiurban,1.0
LP002138,Male,1.0,0,1,0.0,2625,6250.0,187.0,360.0,1,Rural,1.0
LP002139,Male,1.0,0,1,0.0,9083,0.0,228.0,360.0,1,Semiurban,1.0
LP002140,Male,0.0,0,1,0.0,8750,4167.0,308.0,360.0,1,Rural,0.0
LP002141,Male,1.0,3+,1,0.0,2666,2083.0,95.0,360.0,1,Rural,1.0
LP002142,Female,1.0,0,1,1.0,5500,0.0,105.0,360.0,0,Rural,0.0
LP002143,Female,1.0,0,1,0.0,2423,505.0,130.0,360.0,1,Semiurban,1.0
LP002144,Female,0.0,,1,0.0,3813,0.0,116.0,180.0,1,Urban,1.0
LP002149,Male,1.0,2,1,0.0,8333,3167.0,165.0,360.0,1,Rural,1.0
LP002151,Male,1.0,1,1,0.0,3875,0.0,67.0,360.0,1,Urban,0.0
LP002158,Male,1.0,0,0,0.0,3000,1666.0,100.0,480.0,0,Urban,0.0
LP002160,Male,1.0,3+,1,0.0,5167,3167.0,200.0,360.0,1,Semiurban,1.0
LP002161,Female,0.0,1,1,0.0,4723,0.0,81.0,360.0,1,Semiurban,0.0
LP002170,Male,1.0,2,1,0.0,5000,3667.0,236.0,360.0,1,Semiurban,1.0
LP002175,Male,1.0,0,1,0.0,4750,2333.0,130.0,360.0,1,Urban,1.0
LP002178,Male,1.0,0,1,0.0,3013,3033.0,95.0,300.0,0,Urban,1.0
LP002180,Male,0.0,0,1,1.0,6822,0.0,141.0,360.0,1,Rural,1.0
LP002181,Male,0.0,0,0,0.0,6216,0.0,133.0,360.0,1,Rural,0.0
LP002187,Male,0.0,0,1,0.0,2500,0.0,96.0,480.0,1,Semiurban,0.0
LP002188,Male,0.0,0,1,0.0,5124,0.0,124.0,,0,Rural,0.0
LP002190,Male,1.0,1,1,0.0,6325,0.0,175.0,360.0,1,Semiurban,1.0
LP002191,Male,1.0,0,1,0.0,19730,5266.0,570.0,360.0,1,Rural,0.0
LP002194,Female,0.0,0,1,1.0,15759,0.0,55.0,360.0,1,Semiurban,1.0
LP002197,Male,1.0,2,1,0.0,5185,0.0,155.0,360.0,1,Semiurban,1.0
LP002201,Male,1.0,2,1,1.0,9323,7873.0,380.0,300.0,1,Rural,1.0
LP002205,Male,0.0,1,1,0.0,3062,1987.0,111.0,180.0,0,Urban,0.0
LP002209,Female,0.0,0,1,0.0,2764,1459.0,110.0,360.0,1,Urban,1.0
LP002211,Male,1.0,0,1,0.0,4817,923.0,120.0,180.0,1,Urban,1.0
LP002219,Male,1.0,3+,1,0.0,8750,4996.0,130.0,360.0,1,Rural,1.0
LP002223,Male,1.0,0,1,0.0,4310,0.0,130.0,360.0,0,Semiurban,1.0
LP002224,Male,0.0,0,1,0.0,3069,0.0,71.0,480.0,1,Urban,0.0
LP002225,Male,1.0,2,1,0.0,5391,0.0,130.0,360.0,1,Urban,1.0
LP002226,Male,1.0,0,1,0.0,3333,2500.0,128.0,360.0,1,Semiurban,1.0
LP002229,Male,0.0,0,1,0.0,5941,4232.0,296.0,360.0,1,Semiurban,1.0
LP002231,Female,0.0,0,1,0.0,6000,0.0,156.0,360.0,1,Urban,1.0
LP002234,Male,0.0,0,1,1.0,7167,0.0,128.0,360.0,1,Urban,1.0
LP002236,Male,1.0,2,1,0.0,4566,0.0,100.0,360.0,1,Urban,0.0
LP002237,Male,0.0,1,1,0.0,3667,0.0,113.0,180.0,1,Urban,1.0
LP002239,Male,0.0,0,0,0.0,2346,1600.0,132.0,360.0,1,Semiurban,1.0
LP002243,Male,1.0,0,0,0.0,3010,3136.0,0.0,360.0,0,Urban,0.0
LP002244,Male,1.0,0,1,0.0,2333,2417.0,136.0,360.0,1,Urban,1.0
LP002250,Male,1.0,0,1,0.0,5488,0.0,125.0,360.0,1,Rural,1.0
LP002255,Male,0.0,3+,1,0.0,9167,0.0,185.0,360.0,1,Rural,1.0
LP002262,Male,1.0,3+,1,0.0,9504,0.0,275.0,360.0,1,Rural,1.0
LP002263,Male,1.0,0,1,0.0,2583,2115.0,120.0,360.0,0,Urban,1.0
LP002265,Male,1.0,2,0,0.0,1993,1625.0,113.0,180.0,1,Semiurban,1.0
LP002266,Male,1.0,2,1,0.0,3100,1400.0,113.0,360.0,1,Urban,1.0
LP002272,Male,1.0,2,1,0.0,3276,484.0,135.0,360.0,0,Semiurban,1.0
LP002277,Female,0.0,0,1,0.0,3180,0.0,71.0,360.0,0,Urban,0.0
LP002281,Male,1.0,0,1,0.0,3033,1459.0,95.0,360.0,1,Urban,1.0
LP002284,Male,0.0,0,0,0.0,3902,1666.0,109.0,360.0,1,Rural,1.0
LP002287,Female,0.0,0,1,0.0,1500,1800.0,103.0,360.0,0,Semiurban,0.0
LP002288,Male,1.0,2,0,0.0,2889,0.0,45.0,180.0,0,Urban,0.0
LP002296,Male,0.0,0,0,0.0,2755,0.0,65.0,300.0,1,Rural,0.0
LP002297,Male,0.0,0,1,0.0,2500,20000.0,103.0,360.0,1,Semiurban,1.0
LP002300,Female,0.0,0,0,0.0,1963,0.0,53.0,360.0,1,Semiurban,1.0
LP002301,Female,0.0,0,1,1.0,7441,0.0,194.0,360.0,1,Rural,0.0
LP002305,Female,0.0,0,1,0.0,4547,0.0,115.0,360.0,1,Semiurban,1.0
LP002308,Male,1.0,0,0,0.0,2167,2400.0,115.0,360.0,1,Urban,1.0
LP002314,Female,0.0,0,0,0.0,2213,0.0,66.0,360.0,1,Rural,1.0
LP002315,Male,1.0,1,1,0.0,8300,0.0,152.0,300.0,0,Semiurban,0.0
LP002317,Male,1.0,3+,1,0.0,81000,0.0,360.0,360.0,0,Rural,0.0
LP002318,Female,0.0,1,0,1.0,3867,0.0,62.0,360.0,1,Semiurban,0.0
LP002319,Male,1.0,0,1,0.0,6256,0.0,160.0,360.0,0,Urban,1.0
LP002328,Male,1.0,0,0,0.0,6096,0.0,218.0,360.0,0,Rural,0.0
LP002332,Male,1.0,0,0,0.0,2253,2033.0,110.0,360.0,1,Rural,1.0
LP002335,Female,1.0,0,0,0.0,2149,3237.0,178.0,360.0,0,Semiurban,0.0
LP002337,Female,0.0,0,1,0.0,2995,0.0,60.0,360.0,1,Urban,1.0
LP002341,Female,0.0,1,1,0.0,2600,0.0,160.0,360.0,1,Urban,0.0
LP002342,Male,1.0,2,1,1.0,1600,20000.0,239.0,360.0,1,Urban,0.0
LP002345,Male,1.0,0,1,0.0,1025,2773.0,112.0,360.0,1,Rural,1.0
LP002347,Male,1.0,0,1,0.0,3246,1417.0,138.0,360.0,1,Semiurban,1.0
LP002348,Male,1.0,0,1,0.0,5829,0.0,138.0,360.0,1,Rural,1.0
LP002357,Female,0.0,0,0,0.0,2720,0.0,80.0,,0,Urban,0.0
LP002361,Male,1.0,0,1,0.0,1820,1719.0,100.0,360.0,1,Urban,1.0
LP002362,Male,1.0,1,1,0.0,7250,1667.0,110.0,,0,Urban,0.0
LP002364,Male,1.0,0,1,0.0,14880,0.0,96.0,360.0,1,Semiurban,1.0
LP002366,Male,1.0,0,1,0.0,2666,4300.0,121.0,360.0,1,Rural,1.0
LP002367,Female,0.0,1,0,0.0,4606,0.0,81.0,360.0,1,Rural,0.0
LP002368,Male,1.0,2,1,0.0,5935,0.0,133.0,360.0,1,Semiurban,1.0
LP002369,Male,1.0,0,1,0.0,2920,16.12000084,87.0,360.0,1,Rural,1.0
LP002370,Male,0.0,0,0,0.0,2717,0.0,60.0,180.0,1,Urban,1.0
LP002377,Female,0.0,1,1,1.0,8624,0.0,150.0,360.0,1,Semiurban,1.0
LP002379,Male,0.0,0,1,0.0,6500,0.0,105.0,360.0,0,Rural,0.0
LP002386,Male,0.0,0,1,0.0,12876,0.0,405.0,360.0,1,Semiurban,1.0
LP002387,Male,1.0,0,1,0.0,2425,2340.0,143.0,360.0,1,Semiurban,1.0
LP002390,Male,0.0,0,1,0.0,3750,0.0,100.0,360.0,1,Urban,1.0
LP002393,Female,0.0,,1,0.0,10047,0.0,0.0,240.0,1,Semiurban,1.0
LP002398,Male,0.0,0,1,0.0,1926,1851.0,50.0,360.0,1,Semiurban,1.0
LP002401,Male,1.0,0,1,0.0,2213,1125.0,0.0,360.0,1,Urban,1.0
LP002403,Male,0.0,0,1,1.0,10416,0.0,187.0,360.0,0,Urban,0.0
LP002407,Female,1.0,0,0,1.0,7142,0.0,138.0,360.0,1,Rural,1.0
LP002408,Male,0.0,0,1,0.0,3660,5064.0,187.0,360.0,1,Semiurban,1.0
LP002409,Male,1.0,0,1,0.0,7901,1833.0,180.0,360.0,1,Rural,1.0
LP002418,Male,0.0,3+,0,0.0,4707,1993.0,148.0,360.0,1,Semiurban,1.0
LP002422,Male,0.0,1,1,0.0,37719,0.0,152.0,360.0,1,Semiurban,1.0
LP002424,Male,1.0,0,1,0.0,7333,8333.0,175.0,300.0,0,Rural,1.0
LP002429,Male,1.0,1,1,1.0,3466,1210.0,130.0,360.0,1,Rural,1.0
LP002434,Male,1.0,2,0,0.0,4652,0.0,110.0,360.0,1,Rural,1.0
LP002435,Male,1.0,0,1,0.0,3539,1376.0,55.0,360.0,1,Rural,0.0
LP002443,Male,1.0,2,1,0.0,3340,1710.0,150.0,360.0,0,Rural,0.0
LP002444,Male,0.0,1,0,1.0,2769,1542.0,190.0,360.0,0,Semiurban,0.0
LP002446,Male,1.0,2,0,0.0,2309,1255.0,125.0,360.0,0,Rural,0.0
LP002447,Male,1.0,2,0,0.0,1958,1456.0,60.0,300.0,0,Urban,1.0
LP002448,Male,1.0,0,1,0.0,3948,1733.0,149.0,360.0,0,Rural,0.0
LP002449,Male,1.0,0,1,0.0,2483,2466.0,90.0,180.0,0,Rural,1.0
LP002453,Male,0.0,0,1,1.0,7085,0.0,84.0,360.0,1,Semiurban,1.0
LP002455,Male,1.0,2,1,0.0,3859,0.0,96.0,360.0,1,Semiurban,1.0
LP002459,Male,1.0,0,1,0.0,4301,0.0,118.0,360.0,1,Urban,1.0
LP002467,Male,1.0,0,1,0.0,3708,2569.0,173.0,360.0,1,Urban,0.0
LP002472,Male,0.0,2,1,0.0,4354,0.0,136.0,360.0,1,Rural,1.0
LP002473,Male,1.0,0,1,0.0,8334,0.0,160.0,360.0,1,Semiurban,0.0
LP002478,,1.0,0,1,1.0,2083,4083.0,160.0,360.0,0,Semiurban,1.0
LP002484,Male,1.0,3+,1,0.0,7740,0.0,128.0,180.0,1,Urban,1.0
LP002487,Male,1.0,0,1,0.0,3015,2188.0,153.0,360.0,1,Rural,1.0
LP002489,Female,0.0,1,0,0.0,5191,0.0,132.0,360.0,1,Semiurban,1.0
LP002493,Male,0.0,0,1,0.0,4166,0.0,98.0,360.0,0,Semiurban,0.0
LP002494,Male,0.0,0,1,0.0,6000,0.0,140.0,360.0,1,Rural,1.0
LP002500,Male,1.0,3+,0,0.0,2947,1664.0,70.0,180.0,0,Urban,0.0
LP002501,,1.0,0,1,0.0,16692,0.0,110.0,360.0,1,Semiurban,1.0
LP002502,Female,1.0,2,0,0.0,210,2917.0,98.0,360.0,1,Semiurban,1.0
LP002505,Male,1.0,0,1,0.0,4333,2451.0,110.0,360.0,1,Urban,0.0
LP002515,Male,1.0,1,1,1.0,3450,2079.0,162.0,360.0,1,Semiurban,1.0
LP002517,Male,1.0,1,0,0.0,2653,1500.0,113.0,180.0,0,Rural,0.0
LP002519,Male,1.0,3+,1,0.0,4691,0.0,100.0,360.0,1,Semiurban,1.0
LP002522,Female,0.0,0,1,1.0,2500,0.0,93.0,360.0,0,Urban,1.0
LP002524,Male,0.0,2,1,0.0,5532,4648.0,162.0,360.0,1,Rural,1.0
LP002527,Male,1.0,2,1,1.0,16525,1014.0,150.0,360.0,1,Rural,1.0
LP002529,Male,1.0,2,1,0.0,6700,1750.0,230.0,300.0,1,Semiurban,1.0
LP002530,,1.0,2,1,0.0,2873,1872.0,132.0,360.0,0,Semiurban,0.0
LP002531,Male,1.0,1,1,1.0,16667,2250.0,86.0,360.0,1,Semiurban,1.0
LP002533,Male,1.0,2,1,0.0,2947,1603.0,0.0,360.0,1,Urban,0.0
LP002534,Female,0.0,0,0,0.0,4350,0.0,154.0,360.0,1,Rural,1.0
LP002536,Male,1.0,3+,0,0.0,3095,0.0,113.0,360.0,1,Rural,1.0
LP002537,Male,1.0,0,1,0.0,2083,3150.0,128.0,360.0,1,Semiurban,1.0
LP002541,Male,1.0,0,1,0.0,10833,0.0,234.0,360.0,1,Semiurban,1.0
LP002543,Male,1.0,2,1,0.0,8333,0.0,246.0,360.0,1,Semiurban,1.0
LP002544,Male,1.0,1,0,0.0,1958,2436.0,131.0,360.0,1,Rural,1.0
LP002545,Male,0.0,2,1,0.0,3547,0.0,80.0,360.0,0,Rural,0.0
LP002547,Male,1.0,1,1,0.0,18333,0.0,500.0,360.0,1,Urban,0.0
LP002555,Male,1.0,2,1,1.0,4583,2083.0,160.0,360.0,1,Semiurban,1.0
LP002556,Male,0.0,0,1,0.0,2435,0.0,75.0,360.0,1,Urban,0.0
LP002560,Male,0.0,0,0,0.0,2699,2785.0,96.0,360.0,0,Semiurban,1.0
LP002562,Male,1.0,1,0,0.0,5333,1131.0,186.0,360.0,0,Urban,1.0
LP002571,Male,0.0,0,0,0.0,3691,0.0,110.0,360.0,1,Rural,1.0
LP002582,Female,0.0,0,0,1.0,17263,0.0,225.0,360.0,1,Semiurban,1.0
LP002585,Male,1.0,0,1,0.0,3597,2157.0,119.0,360.0,0,Rural,0.0
LP002586,Female,1.0,1,1,0.0,3326,913.0,105.0,84.0,1,Semiurban,1.0
LP002587,Male,1.0,0,0,0.0,2600,1700.0,107.0,360.0,1,Rural,1.0
LP002588,Male,1.0,0,1,0.0,4625,2857.0,111.0,12.0,0,Urban,1.0
LP002600,Male,1.0,1,1,1.0,2895,0.0,95.0,360.0,1,Semiurban,1.0
LP002602,Male,0.0,0,1,0.0,6283,4416.0,209.0,360.0,0,Rural,0.0
LP002603,Female,0.0,0,1,0.0,645,3683.0,113.0,480.0,1,Rural,1.0
LP002606,Female,0.0,0,1,0.0,3159,0.0,100.0,360.0,1,Semiurban,1.0
LP002615,Male,1.0,2,1,0.0,4865,5624.0,208.0,360.0,1,Semiurban,1.0
LP002618,Male,1.0,1,0,0.0,4050,5302.0,138.0,360.0,0,Rural,0.0
LP002619,Male,1.0,0,0,0.0,3814,1483.0,124.0,300.0,1,Semiurban,1.0
LP002622,Male,1.0,2,1,0.0,3510,4416.0,243.0,360.0,1,Rural,1.0
LP002624,Male,1.0,0,1,0.0,20833,6667.0,480.0,360.0,0,Urban,1.0
LP002625,,0.0,0,1,0.0,3583,0.0,96.0,360.0,1,Urban,0.0
LP002626,Male,1.0,0,1,1.0,2479,3013.0,188.0,360.0,1,Urban,1.0
LP002634,Female,0.0,1,1,0.0,13262,0.0,40.0,360.0,1,Urban,1.0
LP002637,Male,0.0,0,0,0.0,3598,1287.0,100.0,360.0,1,Rural,0.0
LP002640,Male,1.0,1,1,0.0,6065,2004.0,250.0,360.0,1,Semiurban,1.0
LP002643,Male,1.0,2,1,0.0,3283,2035.0,148.0,360.0,1,Urban,1.0
LP002648,Male,1.0,0,1,0.0,2130,6666.0,70.0,180.0,1,Semiurban,0.0
LP002652,Male,0.0,0,1,0.0,5815,3666.0,311.0,360.0,1,Rural,0.0
LP002659,Male,1.0,3+,1,0.0,3466,3428.0,150.0,360.0,1,Rural,1.0
LP002670,Female,1.0,2,1,0.0,2031,1632.0,113.0,480.0,1,Semiurban,1.0
LP002682,Male,1.0,,0,0.0,3074,1800.0,123.0,360.0,0,Semiurban,0.0
LP002683,Male,0.0,0,1,0.0,4683,1915.0,185.0,360.0,1,Semiurban,0.0
LP002684,Female,0.0,0,0,0.0,3400,0.0,95.0,360.0,1,Rural,0.0
LP002689,Male,1.0,2,0,0.0,2192,1742.0,45.0,360.0,1,Semiurban,1.0
LP002690,Male,0.0,0,1,0.0,2500,0.0,55.0,360.0,1,Semiurban,1.0
LP002692,Male,1.0,3+,1,1.0,5677,1424.0,100.0,360.0,1,Rural,1.0
LP002693,Male,1.0,2,1,1.0,7948,7166.0,480.0,360.0,1,Rural,1.0
LP002697,Male,0.0,0,1,0.0,4680,2087.0,0.0,360.0,1,Semiurban,0.0
LP002699,Male,1.0,2,1,1.0,17500,0.0,400.0,360.0,1,Rural,1.0
LP002705,Male,1.0,0,1,0.0,3775,0.0,110.0,360.0,1,Semiurban,1.0
LP002706,Male,1.0,1,0,0.0,5285,1430.0,161.0,360.0,0,Semiurban,1.0
LP002714,Male,0.0,1,0,0.0,2679,1302.0,94.0,360.0,1,Semiurban,1.0
LP002716,Male,0.0,0,0,0.0,6783,0.0,130.0,360.0,1,Semiurban,1.0
LP002717,Male,1.0,0,1,0.0,1025,5500.0,216.0,360.0,0,Rural,1.0
LP002720,Male,1.0,3+,1,0.0,4281,0.0,100.0,360.0,1,Urban,1.0
LP002723,Male,0.0,2,1,0.0,3588,0.0,110.0,360.0,0,Rural,0.0
LP002729,Male,0.0,1,1,0.0,11250,0.0,196.0,360.0,0,Semiurban,0.0
LP002731,Female,0.0,0,0,1.0,18165,0.0,125.0,360.0,1,Urban,1.0
LP002732,Male,0.0,0,0,0.0,2550,2042.0,126.0,360.0,1,Rural,1.0
LP002734,Male,1.0,0,1,0.0,6133,3906.0,324.0,360.0,1,Urban,1.0
LP002738,Male,0.0,2,1,0.0,3617,0.0,107.0,360.0,1,Semiurban,1.0
LP002739,Male,1.0,0,0,0.0,2917,536.0,66.0,360.0,1,Rural,0.0
LP002740,Male,1.0,3+,1,0.0,6417,0.0,157.0,180.0,1,Rural,1.0
LP002741,Female,1.0,1,1,0.0,4608,2845.0,140.0,180.0,1,Semiurban,1.0
LP002743,Female,0.0,0,1,0.0,2138,0.0,99.0,360.0,0,Semiurban,0.0
LP002753,Female,0.0,1,1,0.0,3652,0.0,95.0,360.0,1,Semiurban,1.0
LP002755,Male,1.0,1,0,0.0,2239,2524.0,128.0,360.0,1,Urban,1.0
LP002757,Female,1.0,0,0,0.0,3017,663.0,102.0,360.0,0,Semiurban,1.0
LP002767,Male,1.0,0,1,0.0,2768,1950.0,155.0,360.0,1,Rural,1.0
LP002768,Male,0.0,0,0,0.0,3358,0.0,80.0,36.0,1,Semiurban,0.0
LP002772,Male,0.0,0,1,0.0,2526,1783.0,145.0,360.0,1,Rural,1.0
LP002776,Female,0.0,0,1,0.0,5000,0.0,103.0,360.0,0,Semiurban,0.0
LP002777,Male,1.0,0,1,0.0,2785,2016.0,110.0,360.0,1,Rural,1.0
LP002778,Male,1.0,2,1,1.0,6633,0.0,0.0,360.0,0,Rural,0.0
LP002784,Male,1.0,1,0,0.0,2492,2375.0,0.0,360.0,1,Rural,1.0
LP002785,Male,1.0,1,1,0.0,3333,3250.0,158.0,360.0,1,Urban,1.0
LP002788,Male,1.0,0,0,0.0,2454,2333.0,181.0,360.0,0,Urban,0.0
LP002789,Male,1.0,0,1,0.0,3593,4266.0,132.0,180.0,0,Rural,0.0
LP002792,Male,1.0,1,1,0.0,5468,1032.0,26.0,360.0,1,Semiurban,1.0
LP002794,Female,0.0,0,1,0.0,2667,1625.0,84.0,360.0,0,Urban,1.0
LP002795,Male,1.0,3+,1,1.0,10139,0.0,260.0,360.0,1,Semiurban,1.0
LP002798,Male,1.0,0,1,0.0,3887,2669.0,162.0,360.0,1,Semiurban,1.0
LP002804,Female,1.0,0,1,0.0,4180,2306.0,182.0,360.0,1,Semiurban,1.0
LP002807,Male,1.0,2,0,0.0,3675,242.0,108.0,360.0,1,Semiurban,1.0
LP002813,Female,1.0,1,1,1.0,19484,0.0,600.0,360.0,1,Semiurban,1.0
LP002820,Male,1.0,0,1,0.0,5923,2054.0,211.0,360.0,1,Rural,1.0
LP002821,Male,0.0,0,0,1.0,5800,0.0,132.0,360.0,1,Semiurban,1.0
LP002832,Male,1.0,2,1,0.0,8799,0.0,258.0,360.0,0,Urban,0.0
LP002833,Male,1.0,0,0,0.0,4467,0.0,120.0,360.0,0,Rural,1.0
LP002836,Male,0.0,0,1,0.0,3333,0.0,70.0,360.0,1,Urban,1.0
LP002837,Male,1.0,3+,1,0.0,3400,2500.0,123.0,360.0,0,Rural,0.0
LP002840,Female,0.0,0,1,0.0,2378,0.0,9.0,360.0,1,Urban,0.0
LP002841,Male,1.0,0,1,0.0,3166,2064.0,104.0,360.0,0,Urban,0.0
LP002842,Male,1.0,1,1,0.0,3417,1750.0,186.0,360.0,1,Urban,1.0
LP002847,Male,1.0,,1,0.0,5116,1451.0,165.0,360.0,0,Urban,0.0
LP002855,Male,1.0,2,1,0.0,16666,0.0,275.0,360.0,1,Urban,1.0
LP002862,Male,1.0,2,0,0.0,6125,1625.0,187.0,480.0,1,Semiurban,0.0
LP002863,Male,1.0,3+,1,0.0,6406,0.0,150.0,360.0,1,Semiurban,0.0
LP002868,Male,1.0,2,1,0.0,3159,461.0,108.0,84.0,1,Urban,1.0
LP002872,,1.0,0,1,0.0,3087,2210.0,136.0,360.0,0,Semiurban,0.0
LP002874,Male,0.0,0,1,0.0,3229,2739.0,110.0,360.0,1,Urban,1.0
LP002877,Male,1.0,1,1,0.0,1782,2232.0,107.0,360.0,1,Rural,1.0
LP002888,Male,0.0,0,1,0.0,3182,2917.0,161.0,360.0,1,Urban,1.0
LP002892,Male,1.0,2,1,0.0,6540,0.0,205.0,360.0,1,Semiurban,1.0
LP002893,Male,0.0,0,1,0.0,1836,33837.0,90.0,360.0,1,Urban,0.0
LP002894,Female,1.0,0,1,0.0,3166,0.0,36.0,360.0,1,Semiurban,1.0
LP002898,Male,1.0,1,1,0.0,1880,0.0,61.0,360.0,0,Rural,0.0
LP002911,Male,1.0,1,1,0.0,2787,1917.0,146.0,360.0,0,Rural,0.0
LP002912,Male,1.0,1,1,0.0,4283,3000.0,172.0,84.0,1,Rural,0.0
LP002916,Male,1.0,0,1,0.0,2297,1522.0,104.0,360.0,1,Urban,1.0
LP002917,Female,0.0,0,0,0.0,2165,0.0,70.0,360.0,1,Semiurban,1.0
LP002925,,0.0,0,1,0.0,4750,0.0,94.0,360.0,1,Semiurban,1.0
LP002926,Male,1.0,2,1,1.0,2726,0.0,106.0,360.0,0,Semiurban,0.0
LP002928,Male,1.0,0,1,0.0,3000,3416.0,56.0,180.0,1,Semiurban,1.0
LP002931,Male,1.0,2,1,1.0,6000,0.0,205.0,240.0,1,Semiurban,0.0
LP002933,,0.0,3+,1,1.0,9357,0.0,292.0,360.0,1,Semiurban,1.0
LP002936,Male,1.0,0,1,0.0,3859,3300.0,142.0,180.0,1,Rural,1.0
LP002938,Male,1.0,0,1,1.0,16120,0.0,260.0,360.0,1,Urban,1.0
LP002940,Male,0.0,0,0,0.0,3833,0.0,110.0,360.0,1,Rural,1.0
LP002941,Male,1.0,2,0,1.0,6383,1000.0,187.0,360.0,1,Rural,0.0
LP002943,Male,0.0,,1,0.0,2987,0.0,88.0,360.0,0,Semiurban,0.0
LP002945,Male,1.0,0,1,1.0,9963,0.0,180.0,360.0,1,Rural,1.0
LP002948,Male,1.0,2,1,0.0,5780,0.0,192.0,360.0,1,Urban,1.0
LP002949,Female,0.0,3+,1,0.0,416,41667.0,350.0,180.0,0,Urban,0.0
LP002950,Male,1.0,0,0,0.0,2894,2792.0,155.0,360.0,1,Rural,1.0
LP002953,Male,1.0,3+,1,0.0,5703,0.0,128.0,360.0,1,Urban,1.0
LP002958,Male,0.0,0,1,0.0,3676,4301.0,172.0,360.0,1,Rural,1.0
LP002959,Female,1.0,1,1,0.0,12000,0.0,496.0,360.0,1,Semiurban,1.0
LP002960,Male,1.0,0,0,0.0,2400,3800.0,0.0,180.0,1,Urban,0.0
LP002961,Male,1.0,1,1,0.0,3400,2500.0,173.0,360.0,1,Semiurban,1.0
LP002964,Male,1.0,2,0,0.0,3987,1411.0,157.0,360.0,1,Rural,1.0
LP002974,Male,1.0,0,1,0.0,3232,1950.0,108.0,360.0,1,Rural,1.0
LP002978,Female,0.0,0,1,0.0,2900,0.0,71.0,360.0,1,Rural,1.0
LP002979,Male,1.0,3+,1,0.0,4106,0.0,40.0,180.0,1,Rural,1.0
LP002983,Male,1.0,1,1,0.0,8072,240.0,253.0,360.0,1,Urban,1.0
LP002984,Male,1.0,2,1,0.0,7583,0.0,187.0,360.0,1,Urban,1.0
LP002990,Female,0.0,0,1,1.0,4583,0.0,133.0,360.0,0,Semiurban,0.0
1 Loan_ID Gender Married Dependents Education Self_Employed ApplicantIncome CoapplicantIncome LoanAmount Loan_Amount_Term Credit_History Property_Area Loan_Status
2 LP001002 Male 0.0 0 1 0.0 5849 0.0 360.0 1.0 0 Y 0.0
3 LP001003 Male 1.0 1 1 0.0 4583 1508.0 128.0 360.0 1 Rural 0.0
4 LP001005 Male 1.0 0 1 1.0 3000 0.0 66.0 360.0 1 Urban 1.0
5 LP001006 Male 1.0 0 0 0.0 2583 2358.0 120.0 360.0 1 Urban 1.0
6 LP001008 Male 0.0 0 1 0.0 6000 0.0 141.0 360.0 1 Urban 1.0
7 LP001011 Male 1.0 2 1 1.0 5417 4196.0 267.0 360.0 1 Urban 1.0
8 LP001013 Male 1.0 0 0 0.0 2333 1516.0 95.0 360.0 1 Urban 1.0
9 LP001014 Male 1.0 3+ 1 0.0 3036 2504.0 158.0 360.0 0 Semiurban 0.0
10 LP001018 Male 1.0 2 1 0.0 4006 1526.0 168.0 360.0 1 Urban 1.0
11 LP001020 Male 1.0 1 1 0.0 12841 10968.0 349.0 360.0 1 Semiurban 0.0
12 LP001024 Male 1.0 2 1 0.0 3200 700.0 70.0 360.0 1 Urban 1.0
13 LP001027 Male 1.0 2 1 0.0 2500 1840.0 109.0 360.0 1 Urban 1.0
14 LP001028 Male 1.0 2 1 0.0 3073 8106.0 200.0 360.0 1 Urban 1.0
15 LP001029 Male 0.0 0 1 0.0 1853 2840.0 114.0 360.0 1 Rural 0.0
16 LP001030 Male 1.0 2 1 0.0 1299 1086.0 17.0 120.0 1 Urban 1.0
17 LP001032 Male 0.0 0 1 0.0 4950 0.0 125.0 360.0 1 Urban 1.0
18 LP001034 Male 0.0 1 0 0.0 3596 0.0 100.0 240.0 0 Urban 1.0
19 LP001036 Female 0.0 0 1 0.0 3510 0.0 76.0 360.0 0 Urban 0.0
20 LP001038 Male 1.0 0 0 0.0 4887 0.0 133.0 360.0 1 Rural 0.0
21 LP001041 Male 1.0 0 1 0.0 2600 3500.0 115.0 1 Urban 1.0
22 LP001043 Male 1.0 0 0 0.0 7660 0.0 104.0 360.0 0 Urban 0.0
23 LP001046 Male 1.0 1 1 0.0 5955 5625.0 315.0 360.0 1 Urban 1.0
24 LP001047 Male 1.0 0 0 0.0 2600 1911.0 116.0 360.0 0 Semiurban 0.0
25 LP001050 1.0 2 0 0.0 3365 1917.0 112.0 360.0 0 Rural 0.0
26 LP001052 Male 1.0 1 1 0.0 3717 2925.0 151.0 360.0 0 Semiurban 0.0
27 LP001066 Male 1.0 0 1 1.0 9560 0.0 191.0 360.0 1 Semiurban 1.0
28 LP001068 Male 1.0 0 1 0.0 2799 2253.0 122.0 360.0 1 Semiurban 1.0
29 LP001073 Male 1.0 2 0 0.0 4226 1040.0 110.0 360.0 1 Urban 1.0
30 LP001086 Male 0.0 0 0 0.0 1442 0.0 35.0 360.0 1 Urban 0.0
31 LP001087 Female 0.0 2 1 0.0 3750 2083.0 120.0 360.0 1 Semiurban 1.0
32 LP001091 Male 1.0 1 1 0.0 4166 3369.0 201.0 360.0 0 Urban 0.0
33 LP001095 Male 0.0 0 1 0.0 3167 0.0 74.0 360.0 1 Urban 0.0
34 LP001097 Male 0.0 1 1 1.0 4692 0.0 106.0 360.0 1 Rural 0.0
35 LP001098 Male 1.0 0 1 0.0 3500 1667.0 114.0 360.0 1 Semiurban 1.0
36 LP001100 Male 0.0 3+ 1 0.0 12500 3000.0 320.0 360.0 1 Rural 0.0
37 LP001106 Male 1.0 0 1 0.0 2275 2067.0 0.0 360.0 1 Urban 1.0
38 LP001109 Male 1.0 0 1 0.0 1828 1330.0 100.0 0 Urban 0.0
39 LP001112 Female 1.0 0 1 0.0 3667 1459.0 144.0 360.0 1 Semiurban 1.0
40 LP001114 Male 0.0 0 1 0.0 4166 7210.0 184.0 360.0 1 Urban 1.0
41 LP001116 Male 0.0 0 0 0.0 3748 1668.0 110.0 360.0 1 Semiurban 1.0
42 LP001119 Male 0.0 0 1 0.0 3600 0.0 80.0 360.0 1 Urban 0.0
43 LP001120 Male 0.0 0 1 0.0 1800 1213.0 47.0 360.0 1 Urban 1.0
44 LP001123 Male 1.0 0 1 0.0 2400 0.0 75.0 360.0 0 Urban 1.0
45 LP001131 Male 1.0 0 1 0.0 3941 2336.0 134.0 360.0 1 Semiurban 1.0
46 LP001136 Male 1.0 0 0 1.0 4695 0.0 96.0 1 Urban 1.0
47 LP001137 Female 0.0 0 1 0.0 3410 0.0 88.0 1 Urban 1.0
48 LP001138 Male 1.0 1 1 0.0 5649 0.0 44.0 360.0 1 Urban 1.0
49 LP001144 Male 1.0 0 1 0.0 5821 0.0 144.0 360.0 1 Urban 1.0
50 LP001146 Female 1.0 0 1 0.0 2645 3440.0 120.0 360.0 0 Urban 0.0
51 LP001151 Female 0.0 0 1 0.0 4000 2275.0 144.0 360.0 1 Semiurban 1.0
52 LP001155 Female 1.0 0 0 0.0 1928 1644.0 100.0 360.0 1 Semiurban 1.0
53 LP001157 Female 0.0 0 1 0.0 3086 0.0 120.0 360.0 1 Semiurban 1.0
54 LP001164 Female 0.0 0 1 0.0 4230 0.0 112.0 360.0 1 Semiurban 0.0
55 LP001179 Male 1.0 2 1 0.0 4616 0.0 134.0 360.0 1 Urban 0.0
56 LP001186 Female 1.0 1 1 1.0 11500 0.0 286.0 360.0 0 Urban 0.0
57 LP001194 Male 1.0 2 1 0.0 2708 1167.0 97.0 360.0 1 Semiurban 1.0
58 LP001195 Male 1.0 0 1 0.0 2132 1591.0 96.0 360.0 1 Semiurban 1.0
59 LP001197 Male 1.0 0 1 0.0 3366 2200.0 135.0 360.0 1 Rural 0.0
60 LP001198 Male 1.0 1 1 0.0 8080 2250.0 180.0 360.0 1 Urban 1.0
61 LP001199 Male 1.0 2 0 0.0 3357 2859.0 144.0 360.0 1 Urban 1.0
62 LP001205 Male 1.0 0 1 0.0 2500 3796.0 120.0 360.0 1 Urban 1.0
63 LP001206 Male 1.0 3+ 1 0.0 3029 0.0 99.0 360.0 1 Urban 1.0
64 LP001207 Male 1.0 0 0 1.0 2609 3449.0 165.0 180.0 0 Rural 0.0
65 LP001213 Male 1.0 1 1 0.0 4945 0.0 0.0 360.0 0 Rural 0.0
66 LP001222 Female 0.0 0 1 0.0 4166 0.0 116.0 360.0 0 Semiurban 0.0
67 LP001225 Male 1.0 0 1 0.0 5726 4595.0 258.0 360.0 1 Semiurban 0.0
68 LP001228 Male 0.0 0 0 0.0 3200 2254.0 126.0 180.0 0 Urban 0.0
69 LP001233 Male 1.0 1 1 0.0 10750 0.0 312.0 360.0 1 Urban 1.0
70 LP001238 Male 1.0 3+ 0 1.0 7100 0.0 125.0 60.0 1 Urban 1.0
71 LP001241 Female 0.0 0 1 0.0 4300 0.0 136.0 360.0 0 Semiurban 0.0
72 LP001243 Male 1.0 0 1 0.0 3208 3066.0 172.0 360.0 1 Urban 1.0
73 LP001245 Male 1.0 2 0 1.0 1875 1875.0 97.0 360.0 1 Semiurban 1.0
74 LP001248 Male 0.0 0 1 0.0 3500 0.0 81.0 300.0 1 Semiurban 1.0
75 LP001250 Male 1.0 3+ 0 0.0 4755 0.0 95.0 0 Semiurban 0.0
76 LP001253 Male 1.0 3+ 1 1.0 5266 1774.0 187.0 360.0 1 Semiurban 1.0
77 LP001255 Male 0.0 0 1 0.0 3750 0.0 113.0 480.0 1 Urban 0.0
78 LP001256 Male 0.0 0 1 0.0 3750 4750.0 176.0 360.0 1 Urban 0.0
79 LP001259 Male 1.0 1 1 1.0 1000 3022.0 110.0 360.0 1 Urban 0.0
80 LP001263 Male 1.0 3+ 1 0.0 3167 4000.0 180.0 300.0 0 Semiurban 0.0
81 LP001264 Male 1.0 3+ 0 1.0 3333 2166.0 130.0 360.0 0 Semiurban 1.0
82 LP001265 Female 0.0 0 1 0.0 3846 0.0 111.0 360.0 1 Semiurban 1.0
83 LP001266 Male 1.0 1 1 1.0 2395 0.0 0.0 360.0 1 Semiurban 1.0
84 LP001267 Female 1.0 2 1 0.0 1378 1881.0 167.0 360.0 1 Urban 0.0
85 LP001273 Male 1.0 0 1 0.0 6000 2250.0 265.0 360.0 0 Semiurban 0.0
86 LP001275 Male 1.0 1 1 0.0 3988 0.0 50.0 240.0 1 Urban 1.0
87 LP001279 Male 0.0 0 1 0.0 2366 2531.0 136.0 360.0 1 Semiurban 1.0
88 LP001280 Male 1.0 2 0 0.0 3333 2000.0 99.0 360.0 0 Semiurban 1.0
89 LP001282 Male 1.0 0 1 0.0 2500 2118.0 104.0 360.0 1 Semiurban 1.0
90 LP001289 Male 0.0 0 1 0.0 8566 0.0 210.0 360.0 1 Urban 1.0
91 LP001310 Male 1.0 0 1 0.0 5695 4167.0 175.0 360.0 1 Semiurban 1.0
92 LP001316 Male 1.0 0 1 0.0 2958 2900.0 131.0 360.0 1 Semiurban 1.0
93 LP001318 Male 1.0 2 1 0.0 6250 5654.0 188.0 180.0 1 Semiurban 1.0
94 LP001319 Male 1.0 2 0 0.0 3273 1820.0 81.0 360.0 1 Urban 1.0
95 LP001322 Male 0.0 0 1 0.0 4133 0.0 122.0 360.0 1 Semiurban 1.0
96 LP001325 Male 0.0 0 0 0.0 3620 0.0 25.0 120.0 1 Semiurban 1.0
97 LP001326 Male 0.0 0 1 0.0 6782 0.0 0.0 360.0 0 Urban 0.0
98 LP001327 Female 1.0 0 1 0.0 2484 2302.0 137.0 360.0 1 Semiurban 1.0
99 LP001333 Male 1.0 0 1 0.0 1977 997.0 50.0 360.0 1 Semiurban 1.0
100 LP001334 Male 1.0 0 0 0.0 4188 0.0 115.0 180.0 1 Semiurban 1.0
101 LP001343 Male 1.0 0 1 0.0 1759 3541.0 131.0 360.0 1 Semiurban 1.0
102 LP001345 Male 1.0 2 0 0.0 4288 3263.0 133.0 180.0 1 Urban 1.0
103 LP001349 Male 0.0 0 1 0.0 4843 3806.0 151.0 360.0 1 Semiurban 1.0
104 LP001350 Male 1.0 1 0.0 13650 0.0 0.0 360.0 1 Urban 1.0
105 LP001356 Male 1.0 0 1 0.0 4652 3583.0 0.0 360.0 1 Semiurban 1.0
106 LP001357 Male 0.0 1 0.0 3816 754.0 160.0 360.0 1 Urban 1.0
107 LP001367 Male 1.0 1 1 0.0 3052 1030.0 100.0 360.0 1 Urban 1.0
108 LP001369 Male 1.0 2 1 0.0 11417 1126.0 225.0 360.0 1 Urban 1.0
109 LP001370 Male 0.0 0 0 0.0 7333 0.0 120.0 360.0 1 Rural 0.0
110 LP001379 Male 1.0 2 1 0.0 3800 3600.0 216.0 360.0 0 Urban 0.0
111 LP001384 Male 1.0 3+ 0 0.0 2071 754.0 94.0 480.0 1 Semiurban 1.0
112 LP001385 Male 0.0 0 1 0.0 5316 0.0 136.0 360.0 1 Urban 1.0
113 LP001387 Female 1.0 0 1 0.0 2929 2333.0 139.0 360.0 1 Semiurban 1.0
114 LP001391 Male 1.0 0 0 0.0 3572 4114.0 152.0 0 Rural 0.0
115 LP001392 Female 0.0 1 1 1.0 7451 0.0 0.0 360.0 1 Semiurban 1.0
116 LP001398 Male 0.0 0 1 0.0 5050 0.0 118.0 360.0 1 Semiurban 1.0
117 LP001401 Male 1.0 1 1 0.0 14583 0.0 185.0 180.0 1 Rural 1.0
118 LP001404 Female 1.0 0 1 0.0 3167 2283.0 154.0 360.0 1 Semiurban 1.0
119 LP001405 Male 1.0 1 1 0.0 2214 1398.0 85.0 360.0 0 Urban 1.0
120 LP001421 Male 1.0 0 1 0.0 5568 2142.0 175.0 360.0 1 Rural 0.0
121 LP001422 Female 0.0 0 1 0.0 10408 0.0 259.0 360.0 1 Urban 1.0
122 LP001426 Male 1.0 1 0.0 5667 2667.0 180.0 360.0 1 Rural 1.0
123 LP001430 Female 0.0 0 1 0.0 4166 0.0 44.0 360.0 1 Semiurban 1.0
124 LP001431 Female 0.0 0 1 0.0 2137 8980.0 137.0 360.0 0 Semiurban 1.0
125 LP001432 Male 1.0 2 1 0.0 2957 0.0 81.0 360.0 1 Semiurban 1.0
126 LP001439 Male 1.0 0 0 0.0 4300 2014.0 194.0 360.0 1 Rural 1.0
127 LP001443 Female 0.0 0 1 0.0 3692 0.0 93.0 360.0 0 Rural 1.0
128 LP001448 1.0 3+ 1 0.0 23803 0.0 370.0 360.0 1 Rural 1.0
129 LP001449 Male 0.0 0 1 0.0 3865 1640.0 0.0 360.0 1 Rural 1.0
130 LP001451 Male 1.0 1 1 1.0 10513 3850.0 160.0 180.0 0 Urban 0.0
131 LP001465 Male 1.0 0 1 0.0 6080 2569.0 182.0 360.0 0 Rural 0.0
132 LP001469 Male 0.0 0 1 1.0 20166 0.0 650.0 480.0 0 Urban 1.0
133 LP001473 Male 0.0 0 1 0.0 2014 1929.0 74.0 360.0 1 Urban 1.0
134 LP001478 Male 0.0 0 1 0.0 2718 0.0 70.0 360.0 1 Semiurban 1.0
135 LP001482 Male 1.0 0 1 1.0 3459 0.0 25.0 120.0 1 Semiurban 1.0
136 LP001487 Male 0.0 0 1 0.0 4895 0.0 102.0 360.0 1 Semiurban 1.0
137 LP001488 Male 1.0 3+ 1 0.0 4000 7750.0 290.0 360.0 1 Semiurban 0.0
138 LP001489 Female 1.0 0 1 0.0 4583 0.0 84.0 360.0 1 Rural 0.0
139 LP001491 Male 1.0 2 1 1.0 3316 3500.0 88.0 360.0 1 Urban 1.0
140 LP001492 Male 0.0 0 1 0.0 14999 0.0 242.0 360.0 0 Semiurban 0.0
141 LP001493 Male 1.0 2 0 0.0 4200 1430.0 129.0 360.0 1 Rural 0.0
142 LP001497 Male 1.0 2 1 0.0 5042 2083.0 185.0 360.0 1 Rural 0.0
143 LP001498 Male 0.0 0 1 0.0 5417 0.0 168.0 360.0 1 Urban 1.0
144 LP001504 Male 0.0 0 1 1.0 6950 0.0 175.0 180.0 1 Semiurban 1.0
145 LP001507 Male 1.0 0 1 0.0 2698 2034.0 122.0 360.0 1 Semiurban 1.0
146 LP001508 Male 1.0 2 1 0.0 11757 0.0 187.0 180.0 1 Urban 1.0
147 LP001514 Female 1.0 0 1 0.0 2330 4486.0 100.0 360.0 1 Semiurban 1.0
148 LP001516 Female 1.0 2 1 0.0 14866 0.0 70.0 360.0 1 Urban 1.0
149 LP001518 Male 1.0 1 1 0.0 1538 1425.0 30.0 360.0 1 Urban 1.0
150 LP001519 Female 0.0 0 1 0.0 10000 1666.0 225.0 360.0 1 Rural 0.0
151 LP001520 Male 1.0 0 1 0.0 4860 830.0 125.0 360.0 1 Semiurban 1.0
152 LP001528 Male 0.0 0 1 0.0 6277 0.0 118.0 360.0 0 Rural 0.0
153 LP001529 Male 1.0 0 1 1.0 2577 3750.0 152.0 360.0 1 Rural 1.0
154 LP001531 Male 0.0 0 1 0.0 9166 0.0 244.0 360.0 1 Urban 0.0
155 LP001532 Male 1.0 2 0 0.0 2281 0.0 113.0 360.0 1 Rural 0.0
156 LP001535 Male 0.0 0 1 0.0 3254 0.0 50.0 360.0 1 Urban 1.0
157 LP001536 Male 1.0 3+ 1 0.0 39999 0.0 600.0 180.0 0 Semiurban 1.0
158 LP001541 Male 1.0 1 1 0.0 6000 0.0 160.0 360.0 0 Rural 1.0
159 LP001543 Male 1.0 1 1 0.0 9538 0.0 187.0 360.0 1 Urban 1.0
160 LP001546 Male 0.0 0 1 0.0 2980 2083.0 120.0 360.0 1 Rural 1.0
161 LP001552 Male 1.0 0 1 0.0 4583 5625.0 255.0 360.0 1 Semiurban 1.0
162 LP001560 Male 1.0 0 0 0.0 1863 1041.0 98.0 360.0 1 Semiurban 1.0
163 LP001562 Male 1.0 0 1 0.0 7933 0.0 275.0 360.0 1 Urban 0.0
164 LP001565 Male 1.0 1 1 0.0 3089 1280.0 121.0 360.0 0 Semiurban 0.0
165 LP001570 Male 1.0 2 1 0.0 4167 1447.0 158.0 360.0 1 Rural 1.0
166 LP001572 Male 1.0 0 1 0.0 9323 0.0 75.0 180.0 1 Urban 1.0
167 LP001574 Male 1.0 0 1 0.0 3707 3166.0 182.0 1 Rural 1.0
168 LP001577 Female 1.0 0 1 0.0 4583 0.0 112.0 360.0 1 Rural 0.0
169 LP001578 Male 1.0 0 1 0.0 2439 3333.0 129.0 360.0 1 Rural 1.0
170 LP001579 Male 0.0 0 1 0.0 2237 0.0 63.0 480.0 0 Semiurban 0.0
171 LP001580 Male 1.0 2 1 0.0 8000 0.0 200.0 360.0 1 Semiurban 1.0
172 LP001581 Male 1.0 0 0 0.0 1820 1769.0 95.0 360.0 1 Rural 1.0
173 LP001585 1.0 3+ 1 0.0 51763 0.0 700.0 300.0 1 Urban 1.0
174 LP001586 Male 1.0 3+ 0 0.0 3522 0.0 81.0 180.0 1 Rural 0.0
175 LP001594 Male 1.0 0 1 0.0 5708 5625.0 187.0 360.0 1 Semiurban 1.0
176 LP001603 Male 1.0 0 0 1.0 4344 736.0 87.0 360.0 1 Semiurban 0.0
177 LP001606 Male 1.0 0 1 0.0 3497 1964.0 116.0 360.0 1 Rural 1.0
178 LP001608 Male 1.0 2 1 0.0 2045 1619.0 101.0 360.0 1 Rural 1.0
179 LP001610 Male 1.0 3+ 1 0.0 5516 11300.0 495.0 360.0 0 Semiurban 0.0
180 LP001616 Male 1.0 1 1 0.0 3750 0.0 116.0 360.0 1 Semiurban 1.0
181 LP001630 Male 0.0 0 0 0.0 2333 1451.0 102.0 480.0 0 Urban 0.0
182 LP001633 Male 1.0 1 1 0.0 6400 7250.0 180.0 360.0 0 Urban 0.0
183 LP001634 Male 0.0 0 1 0.0 1916 5063.0 67.0 360.0 0 Rural 0.0
184 LP001636 Male 1.0 0 1 0.0 4600 0.0 73.0 180.0 1 Semiurban 1.0
185 LP001637 Male 1.0 1 1 0.0 33846 0.0 260.0 360.0 1 Semiurban 0.0
186 LP001639 Female 1.0 0 1 0.0 3625 0.0 108.0 360.0 1 Semiurban 1.0
187 LP001640 Male 1.0 0 1 1.0 39147 4750.0 120.0 360.0 1 Semiurban 1.0
188 LP001641 Male 1.0 1 1 1.0 2178 0.0 66.0 300.0 0 Rural 0.0
189 LP001643 Male 1.0 0 1 0.0 2383 2138.0 58.0 360.0 0 Rural 1.0
190 LP001644 1.0 0 1 1.0 674 5296.0 168.0 360.0 1 Rural 1.0
191 LP001647 Male 1.0 0 1 0.0 9328 0.0 188.0 180.0 1 Rural 1.0
192 LP001653 Male 0.0 0 0 0.0 4885 0.0 48.0 360.0 1 Rural 1.0
193 LP001656 Male 0.0 0 1 0.0 12000 0.0 164.0 360.0 1 Semiurban 0.0
194 LP001657 Male 1.0 0 0 0.0 6033 0.0 160.0 360.0 1 Urban 0.0
195 LP001658 Male 0.0 0 1 0.0 3858 0.0 76.0 360.0 1 Semiurban 1.0
196 LP001664 Male 0.0 0 1 0.0 4191 0.0 120.0 360.0 1 Rural 1.0
197 LP001665 Male 1.0 1 1 0.0 3125 2583.0 170.0 360.0 1 Semiurban 0.0
198 LP001666 Male 0.0 0 1 0.0 8333 3750.0 187.0 360.0 1 Rural 1.0
199 LP001669 Female 0.0 0 0 0.0 1907 2365.0 120.0 1 Urban 1.0
200 LP001671 Female 1.0 0 1 0.0 3416 2816.0 113.0 360.0 0 Semiurban 1.0
201 LP001673 Male 0.0 0 1 1.0 11000 0.0 83.0 360.0 1 Urban 0.0
202 LP001674 Male 1.0 1 0 0.0 2600 2500.0 90.0 360.0 1 Semiurban 1.0
203 LP001677 Male 0.0 2 1 0.0 4923 0.0 166.0 360.0 0 Semiurban 1.0
204 LP001682 Male 1.0 3+ 0 0.0 3992 0.0 0.0 180.0 1 Urban 0.0
205 LP001688 Male 1.0 1 0 0.0 3500 1083.0 135.0 360.0 1 Urban 1.0
206 LP001691 Male 1.0 2 0 0.0 3917 0.0 124.0 360.0 1 Semiurban 1.0
207 LP001692 Female 0.0 0 0 0.0 4408 0.0 120.0 360.0 1 Semiurban 1.0
208 LP001693 Female 0.0 0 1 0.0 3244 0.0 80.0 360.0 1 Urban 1.0
209 LP001698 Male 0.0 0 0 0.0 3975 2531.0 55.0 360.0 1 Rural 1.0
210 LP001699 Male 0.0 0 1 0.0 2479 0.0 59.0 360.0 1 Urban 1.0
211 LP001702 Male 0.0 0 1 0.0 3418 0.0 127.0 360.0 1 Semiurban 0.0
212 LP001708 Female 0.0 0 1 0.0 10000 0.0 214.0 360.0 1 Semiurban 0.0
213 LP001711 Male 1.0 3+ 1 0.0 3430 1250.0 128.0 360.0 0 Semiurban 0.0
214 LP001713 Male 1.0 1 1 1.0 7787 0.0 240.0 360.0 1 Urban 1.0
215 LP001715 Male 1.0 3+ 0 1.0 5703 0.0 130.0 360.0 1 Rural 1.0
216 LP001716 Male 1.0 0 1 0.0 3173 3021.0 137.0 360.0 1 Urban 1.0
217 LP001720 Male 1.0 3+ 0 0.0 3850 983.0 100.0 360.0 1 Semiurban 1.0
218 LP001722 Male 1.0 0 1 0.0 150 1800.0 135.0 360.0 1 Rural 0.0
219 LP001726 Male 1.0 0 1 0.0 3727 1775.0 131.0 360.0 1 Semiurban 1.0
220 LP001732 Male 1.0 2 1 0.0 5000 0.0 72.0 360.0 0 Semiurban 0.0
221 LP001734 Female 1.0 2 1 0.0 4283 2383.0 127.0 360.0 0 Semiurban 1.0
222 LP001736 Male 1.0 0 1 0.0 2221 0.0 60.0 360.0 0 Urban 0.0
223 LP001743 Male 1.0 2 1 0.0 4009 1717.0 116.0 360.0 1 Semiurban 1.0
224 LP001744 Male 0.0 0 1 0.0 2971 2791.0 144.0 360.0 1 Semiurban 1.0
225 LP001749 Male 1.0 0 1 0.0 7578 1010.0 175.0 1 Semiurban 1.0
226 LP001750 Male 1.0 0 1 0.0 6250 0.0 128.0 360.0 1 Semiurban 1.0
227 LP001751 Male 1.0 0 1 0.0 3250 0.0 170.0 360.0 1 Rural 0.0
228 LP001754 Male 1.0 0 1.0 4735 0.0 138.0 360.0 1 Urban 0.0
229 LP001758 Male 1.0 2 1 0.0 6250 1695.0 210.0 360.0 1 Semiurban 1.0
230 LP001760 Male 0.0 1 0.0 4758 0.0 158.0 480.0 1 Semiurban 1.0
231 LP001761 Male 0.0 0 1 1.0 6400 0.0 200.0 360.0 1 Rural 1.0
232 LP001765 Male 1.0 1 1 0.0 2491 2054.0 104.0 360.0 1 Semiurban 1.0
233 LP001768 Male 1.0 0 1 0.0 3716 0.0 42.0 180.0 1 Rural 1.0
234 LP001770 Male 0.0 0 0 0.0 3189 2598.0 120.0 1 Rural 1.0
235 LP001776 Female 0.0 0 1 0.0 8333 0.0 280.0 360.0 1 Semiurban 1.0
236 LP001778 Male 1.0 1 1 0.0 3155 1779.0 140.0 360.0 1 Semiurban 1.0
237 LP001784 Male 1.0 1 1 0.0 5500 1260.0 170.0 360.0 1 Rural 1.0
238 LP001786 Male 1.0 0 1 0.0 5746 0.0 255.0 360.0 0 Urban 0.0
239 LP001788 Female 0.0 0 1 1.0 3463 0.0 122.0 360.0 0 Urban 1.0
240 LP001790 Female 0.0 1 1 0.0 3812 0.0 112.0 360.0 1 Rural 1.0
241 LP001792 Male 1.0 1 1 0.0 3315 0.0 96.0 360.0 1 Semiurban 1.0
242 LP001798 Male 1.0 2 1 0.0 5819 5000.0 120.0 360.0 1 Rural 1.0
243 LP001800 Male 1.0 1 0 0.0 2510 1983.0 140.0 180.0 1 Urban 0.0
244 LP001806 Male 0.0 0 1 0.0 2965 5701.0 155.0 60.0 1 Urban 1.0
245 LP001807 Male 1.0 2 1 1.0 6250 1300.0 108.0 360.0 1 Rural 1.0
246 LP001811 Male 1.0 0 0 0.0 3406 4417.0 123.0 360.0 1 Semiurban 1.0
247 LP001813 Male 0.0 0 1 1.0 6050 4333.0 120.0 180.0 1 Urban 0.0
248 LP001814 Male 1.0 2 1 0.0 9703 0.0 112.0 360.0 1 Urban 1.0
249 LP001819 Male 1.0 1 0 0.0 6608 0.0 137.0 180.0 1 Urban 1.0
250 LP001824 Male 1.0 1 1 0.0 2882 1843.0 123.0 480.0 1 Semiurban 1.0
251 LP001825 Male 1.0 0 1 0.0 1809 1868.0 90.0 360.0 1 Urban 1.0
252 LP001835 Male 1.0 0 0 0.0 1668 3890.0 201.0 360.0 0 Semiurban 0.0
253 LP001836 Female 0.0 2 1 0.0 3427 0.0 138.0 360.0 1 Urban 0.0
254 LP001841 Male 0.0 0 0 1.0 2583 2167.0 104.0 360.0 1 Rural 1.0
255 LP001843 Male 1.0 1 0 0.0 2661 7101.0 279.0 180.0 1 Semiurban 1.0
256 LP001844 Male 0.0 0 1 1.0 16250 0.0 192.0 360.0 0 Urban 0.0
257 LP001846 Female 0.0 3+ 1 0.0 3083 0.0 255.0 360.0 1 Rural 1.0
258 LP001849 Male 0.0 0 0 0.0 6045 0.0 115.0 360.0 0 Rural 0.0
259 LP001854 Male 1.0 3+ 1 0.0 5250 0.0 94.0 360.0 1 Urban 0.0
260 LP001859 Male 1.0 0 1 0.0 14683 2100.0 304.0 360.0 1 Rural 0.0
261 LP001864 Male 1.0 3+ 0 0.0 4931 0.0 128.0 360.0 0 Semiurban 0.0
262 LP001865 Male 1.0 1 1 0.0 6083 4250.0 330.0 360.0 0 Urban 1.0
263 LP001868 Male 0.0 0 1 0.0 2060 2209.0 134.0 360.0 1 Semiurban 1.0
264 LP001870 Female 0.0 1 1 0.0 3481 0.0 155.0 36.0 1 Semiurban 0.0
265 LP001871 Female 0.0 0 1 0.0 7200 0.0 120.0 360.0 1 Rural 1.0
266 LP001872 Male 0.0 0 1 1.0 5166 0.0 128.0 360.0 1 Semiurban 1.0
267 LP001875 Male 0.0 0 1 0.0 4095 3447.0 151.0 360.0 1 Rural 1.0
268 LP001877 Male 1.0 2 1 0.0 4708 1387.0 150.0 360.0 1 Semiurban 1.0
269 LP001882 Male 1.0 3+ 1 0.0 4333 1811.0 160.0 360.0 0 Urban 1.0
270 LP001883 Female 0.0 0 1 0.0 3418 0.0 135.0 360.0 1 Rural 0.0
271 LP001884 Female 0.0 1 1 0.0 2876 1560.0 90.0 360.0 1 Urban 1.0
272 LP001888 Female 0.0 0 1 0.0 3237 0.0 30.0 360.0 1 Urban 1.0
273 LP001891 Male 1.0 0 1 0.0 11146 0.0 136.0 360.0 1 Urban 1.0
274 LP001892 Male 0.0 0 1 0.0 2833 1857.0 126.0 360.0 1 Rural 1.0
275 LP001894 Male 1.0 0 1 0.0 2620 2223.0 150.0 360.0 1 Semiurban 1.0
276 LP001896 Male 1.0 2 1 0.0 3900 0.0 90.0 360.0 1 Semiurban 1.0
277 LP001900 Male 1.0 1 1 0.0 2750 1842.0 115.0 360.0 1 Semiurban 1.0
278 LP001903 Male 1.0 0 1 0.0 3993 3274.0 207.0 360.0 1 Semiurban 1.0
279 LP001904 Male 1.0 0 1 0.0 3103 1300.0 80.0 360.0 1 Urban 1.0
280 LP001907 Male 1.0 0 1 0.0 14583 0.0 436.0 360.0 1 Semiurban 1.0
281 LP001908 Female 1.0 0 0 0.0 4100 0.0 124.0 360.0 0 Rural 1.0
282 LP001910 Male 0.0 1 0 1.0 4053 2426.0 158.0 360.0 0 Urban 0.0
283 LP001914 Male 1.0 0 1 0.0 3927 800.0 112.0 360.0 1 Semiurban 1.0
284 LP001915 Male 1.0 2 1 0.0 2301 985.7999878 78.0 180.0 1 Urban 1.0
285 LP001917 Female 0.0 0 1 0.0 1811 1666.0 54.0 360.0 1 Urban 1.0
286 LP001922 Male 1.0 0 1 0.0 20667 0.0 0.0 360.0 1 Rural 0.0
287 LP001924 Male 0.0 0 1 0.0 3158 3053.0 89.0 360.0 1 Rural 1.0
288 LP001925 Female 0.0 0 1 1.0 2600 1717.0 99.0 300.0 1 Semiurban 0.0
289 LP001926 Male 1.0 0 1 0.0 3704 2000.0 120.0 360.0 1 Rural 1.0
290 LP001931 Female 0.0 0 1 0.0 4124 0.0 115.0 360.0 1 Semiurban 1.0
291 LP001935 Male 0.0 0 1 0.0 9508 0.0 187.0 360.0 1 Rural 1.0
292 LP001936 Male 1.0 0 1 0.0 3075 2416.0 139.0 360.0 1 Rural 1.0
293 LP001938 Male 1.0 2 1 0.0 4400 0.0 127.0 360.0 0 Semiurban 0.0
294 LP001940 Male 1.0 2 1 0.0 3153 1560.0 134.0 360.0 1 Urban 1.0
295 LP001945 Female 0.0 1 0.0 5417 0.0 143.0 480.0 0 Urban 0.0
296 LP001947 Male 1.0 0 1 0.0 2383 3334.0 172.0 360.0 1 Semiurban 1.0
297 LP001949 Male 1.0 3+ 1 0.0 4416 1250.0 110.0 360.0 1 Urban 1.0
298 LP001953 Male 1.0 1 1 0.0 6875 0.0 200.0 360.0 1 Semiurban 1.0
299 LP001954 Female 1.0 1 1 0.0 4666 0.0 135.0 360.0 1 Urban 1.0
300 LP001955 Female 0.0 0 1 0.0 5000 2541.0 151.0 480.0 1 Rural 0.0
301 LP001963 Male 1.0 1 1 0.0 2014 2925.0 113.0 360.0 1 Urban 0.0
302 LP001964 Male 1.0 0 0 0.0 1800 2934.0 93.0 360.0 0 Urban 0.0
303 LP001972 Male 1.0 0 0.0 2875 1750.0 105.0 360.0 1 Semiurban 1.0
304 LP001974 Female 0.0 0 1 0.0 5000 0.0 132.0 360.0 1 Rural 1.0
305 LP001977 Male 1.0 1 1 0.0 1625 1803.0 96.0 360.0 1 Urban 1.0
306 LP001978 Male 0.0 0 1 0.0 4000 2500.0 140.0 360.0 1 Rural 1.0
307 LP001990 Male 0.0 0 0 0.0 2000 0.0 0.0 360.0 1 Urban 0.0
308 LP001993 Female 0.0 0 1 0.0 3762 1666.0 135.0 360.0 1 Rural 1.0
309 LP001994 Female 0.0 0 1 0.0 2400 1863.0 104.0 360.0 0 Urban 0.0
310 LP001996 Male 0.0 0 1 0.0 20233 0.0 480.0 360.0 1 Rural 0.0
311 LP001998 Male 1.0 2 0 0.0 7667 0.0 185.0 360.0 0 Rural 1.0
312 LP002002 Female 0.0 0 1 0.0 2917 0.0 84.0 360.0 1 Semiurban 1.0
313 LP002004 Male 0.0 0 0 0.0 2927 2405.0 111.0 360.0 1 Semiurban 1.0
314 LP002006 Female 0.0 0 1 0.0 2507 0.0 56.0 360.0 1 Rural 1.0
315 LP002008 Male 1.0 2 1 1.0 5746 0.0 144.0 84.0 0 Rural 1.0
316 LP002024 1.0 0 1 0.0 2473 1843.0 159.0 360.0 1 Rural 0.0
317 LP002031 Male 1.0 1 0 0.0 3399 1640.0 111.0 180.0 1 Urban 1.0
318 LP002035 Male 1.0 2 1 0.0 3717 0.0 120.0 360.0 1 Semiurban 1.0
319 LP002036 Male 1.0 0 1 0.0 2058 2134.0 88.0 360.0 0 Urban 1.0
320 LP002043 Female 0.0 1 1 0.0 3541 0.0 112.0 360.0 0 Semiurban 1.0
321 LP002050 Male 1.0 1 1 1.0 10000 0.0 155.0 360.0 1 Rural 0.0
322 LP002051 Male 1.0 0 1 0.0 2400 2167.0 115.0 360.0 1 Semiurban 1.0
323 LP002053 Male 1.0 3+ 1 0.0 4342 189.0 124.0 360.0 1 Semiurban 1.0
324 LP002054 Male 1.0 2 0 0.0 3601 1590.0 0.0 360.0 1 Rural 1.0
325 LP002055 Female 0.0 0 1 0.0 3166 2985.0 132.0 360.0 0 Rural 1.0
326 LP002065 Male 1.0 3+ 1 0.0 15000 0.0 300.0 360.0 1 Rural 1.0
327 LP002067 Male 1.0 1 1 1.0 8666 4983.0 376.0 360.0 0 Rural 0.0
328 LP002068 Male 0.0 0 1 0.0 4917 0.0 130.0 360.0 0 Rural 1.0
329 LP002082 Male 1.0 0 1 1.0 5818 2160.0 184.0 360.0 1 Semiurban 1.0
330 LP002086 Female 1.0 0 1 0.0 4333 2451.0 110.0 360.0 1 Urban 0.0
331 LP002087 Female 0.0 0 1 0.0 2500 0.0 67.0 360.0 1 Urban 1.0
332 LP002097 Male 0.0 1 1 0.0 4384 1793.0 117.0 360.0 1 Urban 1.0
333 LP002098 Male 0.0 0 1 0.0 2935 0.0 98.0 360.0 1 Semiurban 1.0
334 LP002100 Male 0.0 1 0.0 2833 0.0 71.0 360.0 1 Urban 1.0
335 LP002101 Male 1.0 0 1 0.0 63337 0.0 490.0 180.0 1 Urban 1.0
336 LP002103 1.0 1 1 1.0 9833 1833.0 182.0 180.0 1 Urban 1.0
337 LP002106 Male 1.0 1 1.0 5503 4490.0 70.0 1 Semiurban 1.0
338 LP002110 Male 1.0 1 1 0.0 5250 688.0 160.0 360.0 1 Rural 1.0
339 LP002112 Male 1.0 2 1 1.0 2500 4600.0 176.0 360.0 1 Rural 1.0
340 LP002113 Female 0.0 3+ 0 0.0 1830 0.0 0.0 360.0 0 Urban 0.0
341 LP002114 Female 0.0 0 1 0.0 4160 0.0 71.0 360.0 1 Semiurban 1.0
342 LP002115 Male 1.0 3+ 0 0.0 2647 1587.0 173.0 360.0 1 Rural 0.0
343 LP002116 Female 0.0 0 1 0.0 2378 0.0 46.0 360.0 1 Rural 0.0
344 LP002119 Male 1.0 1 0 0.0 4554 1229.0 158.0 360.0 1 Urban 1.0
345 LP002126 Male 1.0 3+ 0 0.0 3173 0.0 74.0 360.0 1 Semiurban 1.0
346 LP002128 Male 1.0 2 1 0.0 2583 2330.0 125.0 360.0 1 Rural 1.0
347 LP002129 Male 1.0 0 1 0.0 2499 2458.0 160.0 360.0 1 Semiurban 1.0
348 LP002130 Male 1.0 0 0.0 3523 3230.0 152.0 360.0 0 Rural 0.0
349 LP002131 Male 1.0 2 0 0.0 3083 2168.0 126.0 360.0 1 Urban 1.0
350 LP002137 Male 1.0 0 1 0.0 6333 4583.0 259.0 360.0 0 Semiurban 1.0
351 LP002138 Male 1.0 0 1 0.0 2625 6250.0 187.0 360.0 1 Rural 1.0
352 LP002139 Male 1.0 0 1 0.0 9083 0.0 228.0 360.0 1 Semiurban 1.0
353 LP002140 Male 0.0 0 1 0.0 8750 4167.0 308.0 360.0 1 Rural 0.0
354 LP002141 Male 1.0 3+ 1 0.0 2666 2083.0 95.0 360.0 1 Rural 1.0
355 LP002142 Female 1.0 0 1 1.0 5500 0.0 105.0 360.0 0 Rural 0.0
356 LP002143 Female 1.0 0 1 0.0 2423 505.0 130.0 360.0 1 Semiurban 1.0
357 LP002144 Female 0.0 1 0.0 3813 0.0 116.0 180.0 1 Urban 1.0
358 LP002149 Male 1.0 2 1 0.0 8333 3167.0 165.0 360.0 1 Rural 1.0
359 LP002151 Male 1.0 1 1 0.0 3875 0.0 67.0 360.0 1 Urban 0.0
360 LP002158 Male 1.0 0 0 0.0 3000 1666.0 100.0 480.0 0 Urban 0.0
361 LP002160 Male 1.0 3+ 1 0.0 5167 3167.0 200.0 360.0 1 Semiurban 1.0
362 LP002161 Female 0.0 1 1 0.0 4723 0.0 81.0 360.0 1 Semiurban 0.0
363 LP002170 Male 1.0 2 1 0.0 5000 3667.0 236.0 360.0 1 Semiurban 1.0
364 LP002175 Male 1.0 0 1 0.0 4750 2333.0 130.0 360.0 1 Urban 1.0
365 LP002178 Male 1.0 0 1 0.0 3013 3033.0 95.0 300.0 0 Urban 1.0
366 LP002180 Male 0.0 0 1 1.0 6822 0.0 141.0 360.0 1 Rural 1.0
367 LP002181 Male 0.0 0 0 0.0 6216 0.0 133.0 360.0 1 Rural 0.0
368 LP002187 Male 0.0 0 1 0.0 2500 0.0 96.0 480.0 1 Semiurban 0.0
369 LP002188 Male 0.0 0 1 0.0 5124 0.0 124.0 0 Rural 0.0
370 LP002190 Male 1.0 1 1 0.0 6325 0.0 175.0 360.0 1 Semiurban 1.0
371 LP002191 Male 1.0 0 1 0.0 19730 5266.0 570.0 360.0 1 Rural 0.0
372 LP002194 Female 0.0 0 1 1.0 15759 0.0 55.0 360.0 1 Semiurban 1.0
373 LP002197 Male 1.0 2 1 0.0 5185 0.0 155.0 360.0 1 Semiurban 1.0
374 LP002201 Male 1.0 2 1 1.0 9323 7873.0 380.0 300.0 1 Rural 1.0
375 LP002205 Male 0.0 1 1 0.0 3062 1987.0 111.0 180.0 0 Urban 0.0
376 LP002209 Female 0.0 0 1 0.0 2764 1459.0 110.0 360.0 1 Urban 1.0
377 LP002211 Male 1.0 0 1 0.0 4817 923.0 120.0 180.0 1 Urban 1.0
378 LP002219 Male 1.0 3+ 1 0.0 8750 4996.0 130.0 360.0 1 Rural 1.0
379 LP002223 Male 1.0 0 1 0.0 4310 0.0 130.0 360.0 0 Semiurban 1.0
380 LP002224 Male 0.0 0 1 0.0 3069 0.0 71.0 480.0 1 Urban 0.0
381 LP002225 Male 1.0 2 1 0.0 5391 0.0 130.0 360.0 1 Urban 1.0
382 LP002226 Male 1.0 0 1 0.0 3333 2500.0 128.0 360.0 1 Semiurban 1.0
383 LP002229 Male 0.0 0 1 0.0 5941 4232.0 296.0 360.0 1 Semiurban 1.0
384 LP002231 Female 0.0 0 1 0.0 6000 0.0 156.0 360.0 1 Urban 1.0
385 LP002234 Male 0.0 0 1 1.0 7167 0.0 128.0 360.0 1 Urban 1.0
386 LP002236 Male 1.0 2 1 0.0 4566 0.0 100.0 360.0 1 Urban 0.0
387 LP002237 Male 0.0 1 1 0.0 3667 0.0 113.0 180.0 1 Urban 1.0
388 LP002239 Male 0.0 0 0 0.0 2346 1600.0 132.0 360.0 1 Semiurban 1.0
389 LP002243 Male 1.0 0 0 0.0 3010 3136.0 0.0 360.0 0 Urban 0.0
390 LP002244 Male 1.0 0 1 0.0 2333 2417.0 136.0 360.0 1 Urban 1.0
391 LP002250 Male 1.0 0 1 0.0 5488 0.0 125.0 360.0 1 Rural 1.0
392 LP002255 Male 0.0 3+ 1 0.0 9167 0.0 185.0 360.0 1 Rural 1.0
393 LP002262 Male 1.0 3+ 1 0.0 9504 0.0 275.0 360.0 1 Rural 1.0
394 LP002263 Male 1.0 0 1 0.0 2583 2115.0 120.0 360.0 0 Urban 1.0
395 LP002265 Male 1.0 2 0 0.0 1993 1625.0 113.0 180.0 1 Semiurban 1.0
396 LP002266 Male 1.0 2 1 0.0 3100 1400.0 113.0 360.0 1 Urban 1.0
397 LP002272 Male 1.0 2 1 0.0 3276 484.0 135.0 360.0 0 Semiurban 1.0
398 LP002277 Female 0.0 0 1 0.0 3180 0.0 71.0 360.0 0 Urban 0.0
399 LP002281 Male 1.0 0 1 0.0 3033 1459.0 95.0 360.0 1 Urban 1.0
400 LP002284 Male 0.0 0 0 0.0 3902 1666.0 109.0 360.0 1 Rural 1.0
401 LP002287 Female 0.0 0 1 0.0 1500 1800.0 103.0 360.0 0 Semiurban 0.0
402 LP002288 Male 1.0 2 0 0.0 2889 0.0 45.0 180.0 0 Urban 0.0
403 LP002296 Male 0.0 0 0 0.0 2755 0.0 65.0 300.0 1 Rural 0.0
404 LP002297 Male 0.0 0 1 0.0 2500 20000.0 103.0 360.0 1 Semiurban 1.0
405 LP002300 Female 0.0 0 0 0.0 1963 0.0 53.0 360.0 1 Semiurban 1.0
406 LP002301 Female 0.0 0 1 1.0 7441 0.0 194.0 360.0 1 Rural 0.0
407 LP002305 Female 0.0 0 1 0.0 4547 0.0 115.0 360.0 1 Semiurban 1.0
408 LP002308 Male 1.0 0 0 0.0 2167 2400.0 115.0 360.0 1 Urban 1.0
409 LP002314 Female 0.0 0 0 0.0 2213 0.0 66.0 360.0 1 Rural 1.0
410 LP002315 Male 1.0 1 1 0.0 8300 0.0 152.0 300.0 0 Semiurban 0.0
411 LP002317 Male 1.0 3+ 1 0.0 81000 0.0 360.0 360.0 0 Rural 0.0
412 LP002318 Female 0.0 1 0 1.0 3867 0.0 62.0 360.0 1 Semiurban 0.0
413 LP002319 Male 1.0 0 1 0.0 6256 0.0 160.0 360.0 0 Urban 1.0
414 LP002328 Male 1.0 0 0 0.0 6096 0.0 218.0 360.0 0 Rural 0.0
415 LP002332 Male 1.0 0 0 0.0 2253 2033.0 110.0 360.0 1 Rural 1.0
416 LP002335 Female 1.0 0 0 0.0 2149 3237.0 178.0 360.0 0 Semiurban 0.0
417 LP002337 Female 0.0 0 1 0.0 2995 0.0 60.0 360.0 1 Urban 1.0
418 LP002341 Female 0.0 1 1 0.0 2600 0.0 160.0 360.0 1 Urban 0.0
419 LP002342 Male 1.0 2 1 1.0 1600 20000.0 239.0 360.0 1 Urban 0.0
420 LP002345 Male 1.0 0 1 0.0 1025 2773.0 112.0 360.0 1 Rural 1.0
421 LP002347 Male 1.0 0 1 0.0 3246 1417.0 138.0 360.0 1 Semiurban 1.0
422 LP002348 Male 1.0 0 1 0.0 5829 0.0 138.0 360.0 1 Rural 1.0
423 LP002357 Female 0.0 0 0 0.0 2720 0.0 80.0 0 Urban 0.0
424 LP002361 Male 1.0 0 1 0.0 1820 1719.0 100.0 360.0 1 Urban 1.0
425 LP002362 Male 1.0 1 1 0.0 7250 1667.0 110.0 0 Urban 0.0
426 LP002364 Male 1.0 0 1 0.0 14880 0.0 96.0 360.0 1 Semiurban 1.0
427 LP002366 Male 1.0 0 1 0.0 2666 4300.0 121.0 360.0 1 Rural 1.0
428 LP002367 Female 0.0 1 0 0.0 4606 0.0 81.0 360.0 1 Rural 0.0
429 LP002368 Male 1.0 2 1 0.0 5935 0.0 133.0 360.0 1 Semiurban 1.0
430 LP002369 Male 1.0 0 1 0.0 2920 16.12000084 87.0 360.0 1 Rural 1.0
431 LP002370 Male 0.0 0 0 0.0 2717 0.0 60.0 180.0 1 Urban 1.0
432 LP002377 Female 0.0 1 1 1.0 8624 0.0 150.0 360.0 1 Semiurban 1.0
433 LP002379 Male 0.0 0 1 0.0 6500 0.0 105.0 360.0 0 Rural 0.0
434 LP002386 Male 0.0 0 1 0.0 12876 0.0 405.0 360.0 1 Semiurban 1.0
435 LP002387 Male 1.0 0 1 0.0 2425 2340.0 143.0 360.0 1 Semiurban 1.0
436 LP002390 Male 0.0 0 1 0.0 3750 0.0 100.0 360.0 1 Urban 1.0
437 LP002393 Female 0.0 1 0.0 10047 0.0 0.0 240.0 1 Semiurban 1.0
438 LP002398 Male 0.0 0 1 0.0 1926 1851.0 50.0 360.0 1 Semiurban 1.0
439 LP002401 Male 1.0 0 1 0.0 2213 1125.0 0.0 360.0 1 Urban 1.0
440 LP002403 Male 0.0 0 1 1.0 10416 0.0 187.0 360.0 0 Urban 0.0
441 LP002407 Female 1.0 0 0 1.0 7142 0.0 138.0 360.0 1 Rural 1.0
442 LP002408 Male 0.0 0 1 0.0 3660 5064.0 187.0 360.0 1 Semiurban 1.0
443 LP002409 Male 1.0 0 1 0.0 7901 1833.0 180.0 360.0 1 Rural 1.0
444 LP002418 Male 0.0 3+ 0 0.0 4707 1993.0 148.0 360.0 1 Semiurban 1.0
445 LP002422 Male 0.0 1 1 0.0 37719 0.0 152.0 360.0 1 Semiurban 1.0
446 LP002424 Male 1.0 0 1 0.0 7333 8333.0 175.0 300.0 0 Rural 1.0
447 LP002429 Male 1.0 1 1 1.0 3466 1210.0 130.0 360.0 1 Rural 1.0
448 LP002434 Male 1.0 2 0 0.0 4652 0.0 110.0 360.0 1 Rural 1.0
449 LP002435 Male 1.0 0 1 0.0 3539 1376.0 55.0 360.0 1 Rural 0.0
450 LP002443 Male 1.0 2 1 0.0 3340 1710.0 150.0 360.0 0 Rural 0.0
451 LP002444 Male 0.0 1 0 1.0 2769 1542.0 190.0 360.0 0 Semiurban 0.0
452 LP002446 Male 1.0 2 0 0.0 2309 1255.0 125.0 360.0 0 Rural 0.0
453 LP002447 Male 1.0 2 0 0.0 1958 1456.0 60.0 300.0 0 Urban 1.0
454 LP002448 Male 1.0 0 1 0.0 3948 1733.0 149.0 360.0 0 Rural 0.0
455 LP002449 Male 1.0 0 1 0.0 2483 2466.0 90.0 180.0 0 Rural 1.0
456 LP002453 Male 0.0 0 1 1.0 7085 0.0 84.0 360.0 1 Semiurban 1.0
457 LP002455 Male 1.0 2 1 0.0 3859 0.0 96.0 360.0 1 Semiurban 1.0
458 LP002459 Male 1.0 0 1 0.0 4301 0.0 118.0 360.0 1 Urban 1.0
459 LP002467 Male 1.0 0 1 0.0 3708 2569.0 173.0 360.0 1 Urban 0.0
460 LP002472 Male 0.0 2 1 0.0 4354 0.0 136.0 360.0 1 Rural 1.0
461 LP002473 Male 1.0 0 1 0.0 8334 0.0 160.0 360.0 1 Semiurban 0.0
462 LP002478 1.0 0 1 1.0 2083 4083.0 160.0 360.0 0 Semiurban 1.0
463 LP002484 Male 1.0 3+ 1 0.0 7740 0.0 128.0 180.0 1 Urban 1.0
464 LP002487 Male 1.0 0 1 0.0 3015 2188.0 153.0 360.0 1 Rural 1.0
465 LP002489 Female 0.0 1 0 0.0 5191 0.0 132.0 360.0 1 Semiurban 1.0
466 LP002493 Male 0.0 0 1 0.0 4166 0.0 98.0 360.0 0 Semiurban 0.0
467 LP002494 Male 0.0 0 1 0.0 6000 0.0 140.0 360.0 1 Rural 1.0
468 LP002500 Male 1.0 3+ 0 0.0 2947 1664.0 70.0 180.0 0 Urban 0.0
469 LP002501 1.0 0 1 0.0 16692 0.0 110.0 360.0 1 Semiurban 1.0
470 LP002502 Female 1.0 2 0 0.0 210 2917.0 98.0 360.0 1 Semiurban 1.0
471 LP002505 Male 1.0 0 1 0.0 4333 2451.0 110.0 360.0 1 Urban 0.0
472 LP002515 Male 1.0 1 1 1.0 3450 2079.0 162.0 360.0 1 Semiurban 1.0
473 LP002517 Male 1.0 1 0 0.0 2653 1500.0 113.0 180.0 0 Rural 0.0
474 LP002519 Male 1.0 3+ 1 0.0 4691 0.0 100.0 360.0 1 Semiurban 1.0
475 LP002522 Female 0.0 0 1 1.0 2500 0.0 93.0 360.0 0 Urban 1.0
476 LP002524 Male 0.0 2 1 0.0 5532 4648.0 162.0 360.0 1 Rural 1.0
477 LP002527 Male 1.0 2 1 1.0 16525 1014.0 150.0 360.0 1 Rural 1.0
478 LP002529 Male 1.0 2 1 0.0 6700 1750.0 230.0 300.0 1 Semiurban 1.0
479 LP002530 1.0 2 1 0.0 2873 1872.0 132.0 360.0 0 Semiurban 0.0
480 LP002531 Male 1.0 1 1 1.0 16667 2250.0 86.0 360.0 1 Semiurban 1.0
481 LP002533 Male 1.0 2 1 0.0 2947 1603.0 0.0 360.0 1 Urban 0.0
482 LP002534 Female 0.0 0 0 0.0 4350 0.0 154.0 360.0 1 Rural 1.0
483 LP002536 Male 1.0 3+ 0 0.0 3095 0.0 113.0 360.0 1 Rural 1.0
484 LP002537 Male 1.0 0 1 0.0 2083 3150.0 128.0 360.0 1 Semiurban 1.0
485 LP002541 Male 1.0 0 1 0.0 10833 0.0 234.0 360.0 1 Semiurban 1.0
486 LP002543 Male 1.0 2 1 0.0 8333 0.0 246.0 360.0 1 Semiurban 1.0
487 LP002544 Male 1.0 1 0 0.0 1958 2436.0 131.0 360.0 1 Rural 1.0
488 LP002545 Male 0.0 2 1 0.0 3547 0.0 80.0 360.0 0 Rural 0.0
489 LP002547 Male 1.0 1 1 0.0 18333 0.0 500.0 360.0 1 Urban 0.0
490 LP002555 Male 1.0 2 1 1.0 4583 2083.0 160.0 360.0 1 Semiurban 1.0
491 LP002556 Male 0.0 0 1 0.0 2435 0.0 75.0 360.0 1 Urban 0.0
492 LP002560 Male 0.0 0 0 0.0 2699 2785.0 96.0 360.0 0 Semiurban 1.0
493 LP002562 Male 1.0 1 0 0.0 5333 1131.0 186.0 360.0 0 Urban 1.0
494 LP002571 Male 0.0 0 0 0.0 3691 0.0 110.0 360.0 1 Rural 1.0
495 LP002582 Female 0.0 0 0 1.0 17263 0.0 225.0 360.0 1 Semiurban 1.0
496 LP002585 Male 1.0 0 1 0.0 3597 2157.0 119.0 360.0 0 Rural 0.0
497 LP002586 Female 1.0 1 1 0.0 3326 913.0 105.0 84.0 1 Semiurban 1.0
498 LP002587 Male 1.0 0 0 0.0 2600 1700.0 107.0 360.0 1 Rural 1.0
499 LP002588 Male 1.0 0 1 0.0 4625 2857.0 111.0 12.0 0 Urban 1.0
500 LP002600 Male 1.0 1 1 1.0 2895 0.0 95.0 360.0 1 Semiurban 1.0
501 LP002602 Male 0.0 0 1 0.0 6283 4416.0 209.0 360.0 0 Rural 0.0
502 LP002603 Female 0.0 0 1 0.0 645 3683.0 113.0 480.0 1 Rural 1.0
503 LP002606 Female 0.0 0 1 0.0 3159 0.0 100.0 360.0 1 Semiurban 1.0
504 LP002615 Male 1.0 2 1 0.0 4865 5624.0 208.0 360.0 1 Semiurban 1.0
505 LP002618 Male 1.0 1 0 0.0 4050 5302.0 138.0 360.0 0 Rural 0.0
506 LP002619 Male 1.0 0 0 0.0 3814 1483.0 124.0 300.0 1 Semiurban 1.0
507 LP002622 Male 1.0 2 1 0.0 3510 4416.0 243.0 360.0 1 Rural 1.0
508 LP002624 Male 1.0 0 1 0.0 20833 6667.0 480.0 360.0 0 Urban 1.0
509 LP002625 0.0 0 1 0.0 3583 0.0 96.0 360.0 1 Urban 0.0
510 LP002626 Male 1.0 0 1 1.0 2479 3013.0 188.0 360.0 1 Urban 1.0
511 LP002634 Female 0.0 1 1 0.0 13262 0.0 40.0 360.0 1 Urban 1.0
512 LP002637 Male 0.0 0 0 0.0 3598 1287.0 100.0 360.0 1 Rural 0.0
513 LP002640 Male 1.0 1 1 0.0 6065 2004.0 250.0 360.0 1 Semiurban 1.0
514 LP002643 Male 1.0 2 1 0.0 3283 2035.0 148.0 360.0 1 Urban 1.0
515 LP002648 Male 1.0 0 1 0.0 2130 6666.0 70.0 180.0 1 Semiurban 0.0
516 LP002652 Male 0.0 0 1 0.0 5815 3666.0 311.0 360.0 1 Rural 0.0
517 LP002659 Male 1.0 3+ 1 0.0 3466 3428.0 150.0 360.0 1 Rural 1.0
518 LP002670 Female 1.0 2 1 0.0 2031 1632.0 113.0 480.0 1 Semiurban 1.0
519 LP002682 Male 1.0 0 0.0 3074 1800.0 123.0 360.0 0 Semiurban 0.0
520 LP002683 Male 0.0 0 1 0.0 4683 1915.0 185.0 360.0 1 Semiurban 0.0
521 LP002684 Female 0.0 0 0 0.0 3400 0.0 95.0 360.0 1 Rural 0.0
522 LP002689 Male 1.0 2 0 0.0 2192 1742.0 45.0 360.0 1 Semiurban 1.0
523 LP002690 Male 0.0 0 1 0.0 2500 0.0 55.0 360.0 1 Semiurban 1.0
524 LP002692 Male 1.0 3+ 1 1.0 5677 1424.0 100.0 360.0 1 Rural 1.0
525 LP002693 Male 1.0 2 1 1.0 7948 7166.0 480.0 360.0 1 Rural 1.0
526 LP002697 Male 0.0 0 1 0.0 4680 2087.0 0.0 360.0 1 Semiurban 0.0
527 LP002699 Male 1.0 2 1 1.0 17500 0.0 400.0 360.0 1 Rural 1.0
528 LP002705 Male 1.0 0 1 0.0 3775 0.0 110.0 360.0 1 Semiurban 1.0
529 LP002706 Male 1.0 1 0 0.0 5285 1430.0 161.0 360.0 0 Semiurban 1.0
530 LP002714 Male 0.0 1 0 0.0 2679 1302.0 94.0 360.0 1 Semiurban 1.0
531 LP002716 Male 0.0 0 0 0.0 6783 0.0 130.0 360.0 1 Semiurban 1.0
532 LP002717 Male 1.0 0 1 0.0 1025 5500.0 216.0 360.0 0 Rural 1.0
533 LP002720 Male 1.0 3+ 1 0.0 4281 0.0 100.0 360.0 1 Urban 1.0
534 LP002723 Male 0.0 2 1 0.0 3588 0.0 110.0 360.0 0 Rural 0.0
535 LP002729 Male 0.0 1 1 0.0 11250 0.0 196.0 360.0 0 Semiurban 0.0
536 LP002731 Female 0.0 0 0 1.0 18165 0.0 125.0 360.0 1 Urban 1.0
537 LP002732 Male 0.0 0 0 0.0 2550 2042.0 126.0 360.0 1 Rural 1.0
538 LP002734 Male 1.0 0 1 0.0 6133 3906.0 324.0 360.0 1 Urban 1.0
539 LP002738 Male 0.0 2 1 0.0 3617 0.0 107.0 360.0 1 Semiurban 1.0
540 LP002739 Male 1.0 0 0 0.0 2917 536.0 66.0 360.0 1 Rural 0.0
541 LP002740 Male 1.0 3+ 1 0.0 6417 0.0 157.0 180.0 1 Rural 1.0
542 LP002741 Female 1.0 1 1 0.0 4608 2845.0 140.0 180.0 1 Semiurban 1.0
543 LP002743 Female 0.0 0 1 0.0 2138 0.0 99.0 360.0 0 Semiurban 0.0
544 LP002753 Female 0.0 1 1 0.0 3652 0.0 95.0 360.0 1 Semiurban 1.0
545 LP002755 Male 1.0 1 0 0.0 2239 2524.0 128.0 360.0 1 Urban 1.0
546 LP002757 Female 1.0 0 0 0.0 3017 663.0 102.0 360.0 0 Semiurban 1.0
547 LP002767 Male 1.0 0 1 0.0 2768 1950.0 155.0 360.0 1 Rural 1.0
548 LP002768 Male 0.0 0 0 0.0 3358 0.0 80.0 36.0 1 Semiurban 0.0
549 LP002772 Male 0.0 0 1 0.0 2526 1783.0 145.0 360.0 1 Rural 1.0
550 LP002776 Female 0.0 0 1 0.0 5000 0.0 103.0 360.0 0 Semiurban 0.0
551 LP002777 Male 1.0 0 1 0.0 2785 2016.0 110.0 360.0 1 Rural 1.0
552 LP002778 Male 1.0 2 1 1.0 6633 0.0 0.0 360.0 0 Rural 0.0
553 LP002784 Male 1.0 1 0 0.0 2492 2375.0 0.0 360.0 1 Rural 1.0
554 LP002785 Male 1.0 1 1 0.0 3333 3250.0 158.0 360.0 1 Urban 1.0
555 LP002788 Male 1.0 0 0 0.0 2454 2333.0 181.0 360.0 0 Urban 0.0
556 LP002789 Male 1.0 0 1 0.0 3593 4266.0 132.0 180.0 0 Rural 0.0
557 LP002792 Male 1.0 1 1 0.0 5468 1032.0 26.0 360.0 1 Semiurban 1.0
558 LP002794 Female 0.0 0 1 0.0 2667 1625.0 84.0 360.0 0 Urban 1.0
559 LP002795 Male 1.0 3+ 1 1.0 10139 0.0 260.0 360.0 1 Semiurban 1.0
560 LP002798 Male 1.0 0 1 0.0 3887 2669.0 162.0 360.0 1 Semiurban 1.0
561 LP002804 Female 1.0 0 1 0.0 4180 2306.0 182.0 360.0 1 Semiurban 1.0
562 LP002807 Male 1.0 2 0 0.0 3675 242.0 108.0 360.0 1 Semiurban 1.0
563 LP002813 Female 1.0 1 1 1.0 19484 0.0 600.0 360.0 1 Semiurban 1.0
564 LP002820 Male 1.0 0 1 0.0 5923 2054.0 211.0 360.0 1 Rural 1.0
565 LP002821 Male 0.0 0 0 1.0 5800 0.0 132.0 360.0 1 Semiurban 1.0
566 LP002832 Male 1.0 2 1 0.0 8799 0.0 258.0 360.0 0 Urban 0.0
567 LP002833 Male 1.0 0 0 0.0 4467 0.0 120.0 360.0 0 Rural 1.0
568 LP002836 Male 0.0 0 1 0.0 3333 0.0 70.0 360.0 1 Urban 1.0
569 LP002837 Male 1.0 3+ 1 0.0 3400 2500.0 123.0 360.0 0 Rural 0.0
570 LP002840 Female 0.0 0 1 0.0 2378 0.0 9.0 360.0 1 Urban 0.0
571 LP002841 Male 1.0 0 1 0.0 3166 2064.0 104.0 360.0 0 Urban 0.0
572 LP002842 Male 1.0 1 1 0.0 3417 1750.0 186.0 360.0 1 Urban 1.0
573 LP002847 Male 1.0 1 0.0 5116 1451.0 165.0 360.0 0 Urban 0.0
574 LP002855 Male 1.0 2 1 0.0 16666 0.0 275.0 360.0 1 Urban 1.0
575 LP002862 Male 1.0 2 0 0.0 6125 1625.0 187.0 480.0 1 Semiurban 0.0
576 LP002863 Male 1.0 3+ 1 0.0 6406 0.0 150.0 360.0 1 Semiurban 0.0
577 LP002868 Male 1.0 2 1 0.0 3159 461.0 108.0 84.0 1 Urban 1.0
578 LP002872 1.0 0 1 0.0 3087 2210.0 136.0 360.0 0 Semiurban 0.0
579 LP002874 Male 0.0 0 1 0.0 3229 2739.0 110.0 360.0 1 Urban 1.0
580 LP002877 Male 1.0 1 1 0.0 1782 2232.0 107.0 360.0 1 Rural 1.0
581 LP002888 Male 0.0 0 1 0.0 3182 2917.0 161.0 360.0 1 Urban 1.0
582 LP002892 Male 1.0 2 1 0.0 6540 0.0 205.0 360.0 1 Semiurban 1.0
583 LP002893 Male 0.0 0 1 0.0 1836 33837.0 90.0 360.0 1 Urban 0.0
584 LP002894 Female 1.0 0 1 0.0 3166 0.0 36.0 360.0 1 Semiurban 1.0
585 LP002898 Male 1.0 1 1 0.0 1880 0.0 61.0 360.0 0 Rural 0.0
586 LP002911 Male 1.0 1 1 0.0 2787 1917.0 146.0 360.0 0 Rural 0.0
587 LP002912 Male 1.0 1 1 0.0 4283 3000.0 172.0 84.0 1 Rural 0.0
588 LP002916 Male 1.0 0 1 0.0 2297 1522.0 104.0 360.0 1 Urban 1.0
589 LP002917 Female 0.0 0 0 0.0 2165 0.0 70.0 360.0 1 Semiurban 1.0
590 LP002925 0.0 0 1 0.0 4750 0.0 94.0 360.0 1 Semiurban 1.0
591 LP002926 Male 1.0 2 1 1.0 2726 0.0 106.0 360.0 0 Semiurban 0.0
592 LP002928 Male 1.0 0 1 0.0 3000 3416.0 56.0 180.0 1 Semiurban 1.0
593 LP002931 Male 1.0 2 1 1.0 6000 0.0 205.0 240.0 1 Semiurban 0.0
594 LP002933 0.0 3+ 1 1.0 9357 0.0 292.0 360.0 1 Semiurban 1.0
595 LP002936 Male 1.0 0 1 0.0 3859 3300.0 142.0 180.0 1 Rural 1.0
596 LP002938 Male 1.0 0 1 1.0 16120 0.0 260.0 360.0 1 Urban 1.0
597 LP002940 Male 0.0 0 0 0.0 3833 0.0 110.0 360.0 1 Rural 1.0
598 LP002941 Male 1.0 2 0 1.0 6383 1000.0 187.0 360.0 1 Rural 0.0
599 LP002943 Male 0.0 1 0.0 2987 0.0 88.0 360.0 0 Semiurban 0.0
600 LP002945 Male 1.0 0 1 1.0 9963 0.0 180.0 360.0 1 Rural 1.0
601 LP002948 Male 1.0 2 1 0.0 5780 0.0 192.0 360.0 1 Urban 1.0
602 LP002949 Female 0.0 3+ 1 0.0 416 41667.0 350.0 180.0 0 Urban 0.0
603 LP002950 Male 1.0 0 0 0.0 2894 2792.0 155.0 360.0 1 Rural 1.0
604 LP002953 Male 1.0 3+ 1 0.0 5703 0.0 128.0 360.0 1 Urban 1.0
605 LP002958 Male 0.0 0 1 0.0 3676 4301.0 172.0 360.0 1 Rural 1.0
606 LP002959 Female 1.0 1 1 0.0 12000 0.0 496.0 360.0 1 Semiurban 1.0
607 LP002960 Male 1.0 0 0 0.0 2400 3800.0 0.0 180.0 1 Urban 0.0
608 LP002961 Male 1.0 1 1 0.0 3400 2500.0 173.0 360.0 1 Semiurban 1.0
609 LP002964 Male 1.0 2 0 0.0 3987 1411.0 157.0 360.0 1 Rural 1.0
610 LP002974 Male 1.0 0 1 0.0 3232 1950.0 108.0 360.0 1 Rural 1.0
611 LP002978 Female 0.0 0 1 0.0 2900 0.0 71.0 360.0 1 Rural 1.0
612 LP002979 Male 1.0 3+ 1 0.0 4106 0.0 40.0 180.0 1 Rural 1.0
613 LP002983 Male 1.0 1 1 0.0 8072 240.0 253.0 360.0 1 Urban 1.0
614 LP002984 Male 1.0 2 1 0.0 7583 0.0 187.0 360.0 1 Urban 1.0
615 LP002990 Female 0.0 0 1 1.0 4583 0.0 133.0 360.0 0 Semiurban 0.0

BIN
abanin_daniil_lab_5/result_1.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 66 KiB

BIN
abanin_daniil_lab_5/result_2.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 22 KiB

82
alexandrov_dmitrii_lab_2/lab2.py Normal file
View File

@ -0,0 +1,82 @@
from sklearn.linear_model import LinearRegression, RandomizedLasso
from sklearn.feature_selection import RFE
from sklearn.preprocessing import MinMaxScaler
from matplotlib import pyplot as plt
import numpy as np
import random as rand
figure = plt.figure(1, figsize=(16, 9))
axis = figure.subplots(1, 4)
col = 0
y = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
def rank_to_dict(ranks, names, n_features):
ranks = np.abs(ranks)
minmax = MinMaxScaler()
ranks = minmax.fit_transform(np.array(ranks).reshape(n_features, 1)).ravel()
ranks = map(lambda x: round(x, 2), ranks)
return dict(zip(names, ranks))
def createView(key, val):
global figure
global axis
global col
global y
axis[col].bar(y, list(val.values()), label=key)
axis[col].set_title(key)
col = col + 1
def start():
np.random.seed(rand.randint(0, 50))
size = 750
n_features = 14
X = np.random.uniform(0, 1, (size, n_features))
Y = (10 * np.sin(np.pi * X[:, 0] * X[:, 1]) + 20 * (X[:, 2] - .5) ** 2 +
10 * X[:, 3] + 5 * X[:, 4] ** 5 + np.random.normal(0, 1))
X[:, 10:] = X[:, :4] + np.random.normal(0, .025, (size, 4))
lr = LinearRegression()
rl = RandomizedLasso()
rfe = RFE(estimator=LinearRegression(), n_features_to_select=1)
lr.fit(X, Y)
rl.fit(X, Y)
rfe.fit(X, Y)
names = ["x%s" % i for i in range(1, n_features + 1)]
rfe_res = rfe.ranking_
for i in range(rfe_res.size):
rfe_res[i] = 14 - rfe_res[i]
ranks = {"Linear regression": rank_to_dict(lr.coef_, names, n_features),
"Random lasso": rank_to_dict(rl.scores_, names, n_features),
"RFE": rank_to_dict(rfe_res, names, n_features)}
mean = {}
for key, value in ranks.items():
for item in value.items():
if item[0] not in mean:
mean[item[0]] = 0
mean[item[0]] += item[1]
for key, value in mean.items():
res = value / len(ranks)
mean[key] = round(res, 2)
ranks["Mean"] = mean
for key, value in ranks.items():
createView(key, value)
ranks[key] = sorted(value.items(), key=lambda y: y[1], reverse=True)
for key, value in ranks.items():
print(key)
print(value)
start()
plt.show()

50
alexandrov_dmitrii_lab_2/readme.md Normal file
View File

@ -0,0 +1,50 @@
### Задание
Выполнить ранжирование признаков с помощью указанных по варианту моделей. Отобразить получившиеся значения\оценки каждого признака каждым методом\моделью и среднюю оценку. Провести анализ получившихся результатов. Определить, какие четыре признака оказались самыми важными по среднему значению.
Вариант 1.
Модели:
* Линейная регрессия (LinearRegression)
* Случайное Лассо (RandomizedLasso)
* Рекурсивное сокращение признаков (Recursive Feature Elimination – RFE)
### Запуск программы
Программа работает на Python 3.7, поскольку только в нём можно подключить нужную версию библиотеки scikit-learn, которая ещё содержит RandomizedLasso.
Файл lab2.py содержит и запускает программу, аргументов и настройки ~~вроде~~ не требует.
### Описание программы
Файл lab2.py содержит непосредственно программу.
Программа создаёт набор данных с 10 признаками для последующего их ранжирования, и обрабатывает тремя моделями по варианту.
Программа строит столбчатые диаграммы, которые показывают как распределились оценки важности признаков, и выводит в консоль отсортированные по убыванию важности признаки.
Таким образом можно легко определить наиважнейшие признаки.
### Результаты тестирования
По результатам тестирования, можно сказать следующее:
* линейная регрессия показывает хорошие результаты, выделяет все 9 значимых признаков.
* случайное лассо справляется хуже других моделей, иногда выделяя шумовые признаки в значимые, а значимые - в шумовые.
* рекурсивное сокращение признаков показывает хорошие результаты, правильно правильно выделяя 9 самых значимых признаков.
* хотя линейная регрессия и рекурсивное сокращение признаков правильно выделяют значимые признаки, саму значимость они оценивают по-разному.
* среднее значение позволяет c хорошей уверенностью определять истинные значимые признаки.
Итого. Если необходимо просто ранжирование, достаточно взять модель RFE, однако, если необходимо анализировать признаки по коэффициентам, имея меру (коэффициенты), то брать нужно линейную регрессию. Случайное лассо лучше не надо.
Пример консольных результатов:
>Linear regression
>[('x1', 1.0), ('x4', 0.69), ('x2', 0.61), ('x11', 0.59), ('x3', 0.51), ('x13', 0.48), ('x5', 0.19), ('x12', 0.19), ('x14', 0.12), ('x8', 0.03), ('x6', 0.02), ('x10', 0.01), ('x7', 0.0), ('x9', 0.0)]
>Random lasso
>[('x5', 1.0), ('x4', 0.76), ('x2', 0.74), ('x1', 0.72), ('x14', 0.44), ('x12', 0.32), ('x11', 0.28), ('x8', 0.22), ('x6', 0.17), ('x3', 0.08), ('x7', 0.02), ('x13', 0.02), ('x9', 0.01), ('x10', 0.0)]
>RFE
>[('x4', 1.0), ('x1', 0.92), ('x11', 0.85), ('x2', 0.77), ('x3', 0.69), ('x13', 0.62), ('x5', 0.54), ('x12', 0.46), ('x14', 0.38), ('x8', 0.31), ('x6', 0.23), ('x10', 0.15), ('x7', 0.08), ('x9', 0.0)]
>Mean
>[('x1', 0.88), ('x4', 0.82), ('x2', 0.71), ('x5', 0.58), ('x11', 0.57), ('x3', 0.43), ('x13', 0.37), ('x12', 0.32), ('x14', 0.31), ('x8', 0.19), ('x6', 0.14), ('x10', 0.05), ('x7', 0.03), ('x9', 0.0)]
По данным результатам можно заключить, что наиболее влиятельные признаки по убыванию: x1, x4, x2, x5.

126
alexandrov_dmitrii_lab_3/lab3.py Normal file
View File

@ -0,0 +1,126 @@
from sklearn.impute import SimpleImputer, MissingIndicator
from sklearn.pipeline import FeatureUnion, make_pipeline
from sklearn.preprocessing import MinMaxScaler
from sklearn.tree import DecisionTreeClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
import pandas as pd
import random as rand
import numpy as np
from matplotlib import pyplot as plt
def rank_to_dict(ranks, names, n_features):
ranks = np.abs(ranks)
minmax = MinMaxScaler()
ranks = minmax.fit_transform(np.array(ranks).reshape(len(ranks), 1)).ravel()
ranks = map(lambda x: round(x, 2), ranks)
return dict(zip(names, ranks))
def part_one():
print('Titanic data analysis\n')
data = pd.read_csv('titanic_data.csv', index_col='PassengerId')
x = data[['Pclass', 'Name', 'Sex']]
y = data[['Survived']]
names = pd.DataFrame(TfidfVectorizer().fit_transform(x['Name']).toarray())
col_names = names[names.columns[1:]].apply(lambda el: sum(el.dropna().astype(float)), axis=1)
col_names.index = np.arange(1, len(col_names) + 1)
col_sexes = []
for index, row in x.iterrows():
if row['Sex'] == 'male':
col_sexes.append(1)
else:
col_sexes.append(0)
x = x.drop(columns=['Sex', 'Name'])
x['Sex'] = col_sexes
x['Name'] = col_names
dtc = DecisionTreeClassifier(random_state=rand.randint(0, 250))
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.05, random_state=rand.randint(0, 250))
dtc.fit(x_train, y_train)
print('model score: ' + str(dtc.score(x_test, y_test)))
res = dict(zip(['Pclass', 'Sex', 'Name'], dtc.feature_importances_))
print('feature importances: ' + str(res))
def part_two():
print('\n---------------------------------------------------------------------------\nSberbank data analysis\n')
data = pd.read_csv('sberbank_data.csv', index_col='id')
x = data.drop(columns='price_doc')
y = data[['price_doc']]
x = x.replace(
['NA', 'no', 'yes', 'Investment', 'OwnerOccupier', 'poor', 'satisfactory', 'no data', 'good', 'excellent'],
[0, 0, 1, 0, 1, -1, 0, 0, 1, 2])
x.fillna(0, inplace=True)
names = pd.DataFrame(TfidfVectorizer().fit_transform(x['sub_area']).toarray())
col_area = names[names.columns[1:]].apply(lambda el: sum(el.dropna().astype(float)), axis=1)
col_area.index = np.arange(1, len(col_area) + 1)
col_date = []
for val in x['timestamp']:
col_date.append(val.split('-', 1)[0])
x = x.drop(columns=['sub_area', 'timestamp'])
x['sub_area'] = col_area
x['timestamp'] = col_date
col_price = []
for val in y['price_doc']:
if val < 1500000:
col_price.append('low')
elif val < 3000000:
col_price.append('medium')
elif val < 5500000:
col_price.append('high')
elif val < 10000000:
col_price.append('premium')
else:
col_price.append('oligarch')
y = pd.DataFrame(col_price)
transformer = FeatureUnion(
transformer_list=[
('features', SimpleImputer(strategy='mean')),
('indicators', MissingIndicator())])
dtr = make_pipeline(transformer, DecisionTreeClassifier(random_state=rand.randint(0, 250)))
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.01, random_state=rand.randint(0, 250))
dtr.fit(x_train, y_train)
features = list(x.columns)
print('model score: ' + str(dtr.score(x_test, y_test)))
res = sorted(dict(zip(features, dtr.steps[-1][1].feature_importances_)).items(),
key=lambda el: el[1], reverse=True)
view_y = []
view_x = []
flag = 0
print('feature importances:')
for val in res:
if flag == 8:
break
print(val[0]+" - "+str(val[1]))
view_y.append(val[0])
view_x.append(val[1])
flag = flag + 1
plt.figure(1, figsize=(16, 9))
plt.bar(view_y, view_x)
plt.show()
def start():
part_one()
part_two()
start()

60
alexandrov_dmitrii_lab_3/readme.md Normal file
View File

@ -0,0 +1,60 @@
### Задание
1. По данным о пассажирах Титаника решить задачу классификации с помощью дерева решений, в которой по различным характеристикам пассажиров требуется найти у выживших пассажиров два наиболее важных признака из трех рассматриваемых.
Вариант 1: Pclass,Name,Sex.
2. По данным курсовой работы с помощью дерева решений решить выбранную задачу: классификация - зависимость категории цены от всех остальных факторов, оценка результата и отбор наиболее значимых признаков.
### Запуск программы
Файл lab3.py содержит и запускает программу, аргументов и настройки ~~вроде~~ не требует.
### Описание программы
Программа состоит из двух частей:
1. Она считывает файл с данными по пассажирам "Титаника", признаки "класс", "имя", "пол" и запись о том, выжил ли пассажир. Данные предобрабатываются: запись о поле кодируется (ж - 0, м - 1), запись об имени кодируется (Tfidf). После этого дерево решений тренируется на данных и результаты выводятся в консоль.
2. Она считывает файл с данными сбербанка по рынку недвижимости. Далее данные предобрабатываются: названия районов кодируется (Tfidf), нечисловые записи цифровизируются, запоняются нулевые записи, записи подразделяются на классы. После этого на данных обучается дерево решений и результат выводится в консоль и на форму. Поскольку признаков слишком много, выводимые результаты ограничены восемью наиболее значимыми.
### Результаты тестирования
По результатам тестирования, можно сказать следующее:
По первой задаче:
* Дерево решений показывает неплохие результаты, около 70-75%.
* Однако оценка важности признаков даёт абсолютно неверный результат: наиболее значимым признаком назначается имя пассажира. Это значит, что кодировка не подходит для правильной обработки данных. Возможные решения: обнуление или исключение признака как аналитически очевидно незначимого.
* Помимо неправильной оценки роли имени, пол определяется более чем в два раза более значимым, нежели класс. Действительная статистика (среди спасшихся пассажиров 74% женщин и детей (из которых многие также были мужского пола) и 26% мужчин, 60% первого класса, 44% - второго, 25% - третьего) скорее подтверждает правильность этого вывода.
По второй задаче:
* Дерево решений показывает неплохие результаты, около 70-75%.
* Оценка важности признаков показывает наиболее важным признаком площадь недвижимости, что скорее всего верно.
* После площади с небольшим отрывом идёт количество спортивных объектов в округе. Это неверно хотя бы потому, что в данных присустствуют коррелирующие признаки - площадь жилого пространства и другие. К тому же доступна информация по действительному ранжированию.
* Дальнейшие оценки содержат как правильные, так и неправильные признаки: этаж, количество этажей в доме, район - действительно значимые признаки, но они перемешаны с незначимыми.
Итого. Дерево решений даёт неплохие результаты при классификации. Однако для задач регрессии не подходят, т.к. неверно определяют значимые признаки. При работе также следует тщательнее предобрабатывать данные, в особенности малозначащие текстовые - предложенные методы кодирования показали себя неэффективно на лабораторных данных.
Пример консольных результатов:
>Titanic data analysis
>model score: 0.7777777777777778
>feature importances: {'Pclass': 0.1287795817634186, 'Sex': 0.3381642167551354, 'Name': 0.533056201481446}
>Sberbank data analysis
>model score: 0.7162629757785467
>feature importances:
>full_sq - 0.1801327274709341
>sport_count_3000 - 0.14881362533480907
>floor - 0.03169232872469085
>power_transmission_line_km - 0.027978416524911377
>timestamp - 0.020092007662845194
>max_floor - 0.019985442431576052
>cafe_count_5000_price_2500 - 0.019397048405749438
>sub_area - 0.017477163456413432

28896
alexandrov_dmitrii_lab_3/sberbank_data.csv Normal file
View File

File diff suppressed because one or more lines are too long

892
alexandrov_dmitrii_lab_3/titanic_data.csv Normal file
View File

@ -0,0 +1,892 @@
PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S
76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S
80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S
81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S
82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S
83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S
85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S
86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S
87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S
88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S
90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S
91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S
92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S
93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S
94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S
95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S
96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S
97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C
98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C
99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S
100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S
101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S
102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S
103,0,1,"White, Mr. Richard Frasar",male,21,0,1,35281,77.2875,D26,S
104,0,3,"Johansson, Mr. Gustaf Joel",male,33,0,0,7540,8.6542,,S
105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37,2,0,3101276,7.925,,S
106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S
107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S
108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S
109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S
110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q
111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S
112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S
114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S
115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S
117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q
118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S
119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C
120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S
121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S
122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C
124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S
125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S
126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C
127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q
128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S
129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C
130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S
131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S
133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47,1,0,A/5. 3337,14.5,,S
134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29,1,0,228414,26,,S
135,0,2,"Sobey, Mr. Samuel James Hayden",male,25,0,0,C.A. 29178,13,,S
136,0,2,"Richard, Mr. Emile",male,23,0,0,SC/PARIS 2133,15.0458,,C
137,1,1,"Newsom, Miss. Helen Monypeny",female,19,0,2,11752,26.2833,D47,S
138,0,1,"Futrelle, Mr. Jacques Heath",male,37,1,0,113803,53.1,C123,S
139,0,3,"Osen, Mr. Olaf Elon",male,16,0,0,7534,9.2167,,S
140,0,1,"Giglio, Mr. Victor",male,24,0,0,PC 17593,79.2,B86,C
141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C
142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S
143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S
144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q
145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S
146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S
147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27,0,0,350043,7.7958,,S
148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9,2,2,W./C. 6608,34.375,,S
149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26,F2,S
150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42,0,0,244310,13,,S
151,0,2,"Bateman, Rev. Robert James",male,51,0,0,S.O.P. 1166,12.525,,S
152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22,1,0,113776,66.6,C2,S
153,0,3,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S
154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S
155,0,3,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S
156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C
157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16,0,0,35851,7.7333,,Q
158,0,3,"Corn, Mr. Harry",male,30,0,0,SOTON/OQ 392090,8.05,,S
159,0,3,"Smiljanic, Mr. Mile",male,,0,0,315037,8.6625,,S
160,0,3,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S
161,0,3,"Cribb, Mr. John Hatfield",male,44,0,1,371362,16.1,,S
162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40,0,0,C.A. 33595,15.75,,S
163,0,3,"Bengtsson, Mr. John Viktor",male,26,0,0,347068,7.775,,S
164,0,3,"Calic, Mr. Jovo",male,17,0,0,315093,8.6625,,S
165,0,3,"Panula, Master. Eino Viljami",male,1,4,1,3101295,39.6875,,S
166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",male,9,0,2,363291,20.525,,S
167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,0,1,113505,55,E33,S
168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45,1,4,347088,27.9,,S
169,0,1,"Baumann, Mr. John D",male,,0,0,PC 17318,25.925,,S
170,0,3,"Ling, Mr. Lee",male,28,0,0,1601,56.4958,,S
171,0,1,"Van der hoef, Mr. Wyckoff",male,61,0,0,111240,33.5,B19,S
172,0,3,"Rice, Master. Arthur",male,4,4,1,382652,29.125,,Q
173,1,3,"Johnson, Miss. Eleanor Ileen",female,1,1,1,347742,11.1333,,S
174,0,3,"Sivola, Mr. Antti Wilhelm",male,21,0,0,STON/O 2. 3101280,7.925,,S
175,0,1,"Smith, Mr. James Clinch",male,56,0,0,17764,30.6958,A7,C
176,0,3,"Klasen, Mr. Klas Albin",male,18,1,1,350404,7.8542,,S
177,0,3,"Lefebre, Master. Henry Forbes",male,,3,1,4133,25.4667,,S
178,0,1,"Isham, Miss. Ann Elizabeth",female,50,0,0,PC 17595,28.7125,C49,C
179,0,2,"Hale, Mr. Reginald",male,30,0,0,250653,13,,S
180,0,3,"Leonard, Mr. Lionel",male,36,0,0,LINE,0,,S
181,0,3,"Sage, Miss. Constance Gladys",female,,8,2,CA. 2343,69.55,,S
182,0,2,"Pernot, Mr. Rene",male,,0,0,SC/PARIS 2131,15.05,,C
183,0,3,"Asplund, Master. Clarence Gustaf Hugo",male,9,4,2,347077,31.3875,,S
184,1,2,"Becker, Master. Richard F",male,1,2,1,230136,39,F4,S
185,1,3,"Kink-Heilmann, Miss. Luise Gretchen",female,4,0,2,315153,22.025,,S
186,0,1,"Rood, Mr. Hugh Roscoe",male,,0,0,113767,50,A32,S
187,1,3,"O'Brien, Mrs. Thomas (Johanna ""Hannah"" Godfrey)",female,,1,0,370365,15.5,,Q
188,1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45,0,0,111428,26.55,,S
189,0,3,"Bourke, Mr. John",male,40,1,1,364849,15.5,,Q
190,0,3,"Turcin, Mr. Stjepan",male,36,0,0,349247,7.8958,,S
191,1,2,"Pinsky, Mrs. (Rosa)",female,32,0,0,234604,13,,S
192,0,2,"Carbines, Mr. William",male,19,0,0,28424,13,,S
193,1,3,"Andersen-Jensen, Miss. Carla Christine Nielsine",female,19,1,0,350046,7.8542,,S
194,1,2,"Navratil, Master. Michel M",male,3,1,1,230080,26,F2,S
195,1,1,"Brown, Mrs. James Joseph (Margaret Tobin)",female,44,0,0,PC 17610,27.7208,B4,C
196,1,1,"Lurette, Miss. Elise",female,58,0,0,PC 17569,146.5208,B80,C
197,0,3,"Mernagh, Mr. Robert",male,,0,0,368703,7.75,,Q
198,0,3,"Olsen, Mr. Karl Siegwart Andreas",male,42,0,1,4579,8.4042,,S
199,1,3,"Madigan, Miss. Margaret ""Maggie""",female,,0,0,370370,7.75,,Q
200,0,2,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24,0,0,248747,13,,S
201,0,3,"Vande Walle, Mr. Nestor Cyriel",male,28,0,0,345770,9.5,,S
202,0,3,"Sage, Mr. Frederick",male,,8,2,CA. 2343,69.55,,S
203,0,3,"Johanson, Mr. Jakob Alfred",male,34,0,0,3101264,6.4958,,S
204,0,3,"Youseff, Mr. Gerious",male,45.5,0,0,2628,7.225,,C
205,1,3,"Cohen, Mr. Gurshon ""Gus""",male,18,0,0,A/5 3540,8.05,,S
206,0,3,"Strom, Miss. Telma Matilda",female,2,0,1,347054,10.4625,G6,S
207,0,3,"Backstrom, Mr. Karl Alfred",male,32,1,0,3101278,15.85,,S
208,1,3,"Albimona, Mr. Nassef Cassem",male,26,0,0,2699,18.7875,,C
209,1,3,"Carr, Miss. Helen ""Ellen""",female,16,0,0,367231,7.75,,Q
210,1,1,"Blank, Mr. Henry",male,40,0,0,112277,31,A31,C
211,0,3,"Ali, Mr. Ahmed",male,24,0,0,SOTON/O.Q. 3101311,7.05,,S
212,1,2,"Cameron, Miss. Clear Annie",female,35,0,0,F.C.C. 13528,21,,S
213,0,3,"Perkin, Mr. John Henry",male,22,0,0,A/5 21174,7.25,,S
214,0,2,"Givard, Mr. Hans Kristensen",male,30,0,0,250646,13,,S
215,0,3,"Kiernan, Mr. Philip",male,,1,0,367229,7.75,,Q
216,1,1,"Newell, Miss. Madeleine",female,31,1,0,35273,113.275,D36,C
217,1,3,"Honkanen, Miss. Eliina",female,27,0,0,STON/O2. 3101283,7.925,,S
218,0,2,"Jacobsohn, Mr. Sidney Samuel",male,42,1,0,243847,27,,S
219,1,1,"Bazzani, Miss. Albina",female,32,0,0,11813,76.2917,D15,C
220,0,2,"Harris, Mr. Walter",male,30,0,0,W/C 14208,10.5,,S
221,1,3,"Sunderland, Mr. Victor Francis",male,16,0,0,SOTON/OQ 392089,8.05,,S
222,0,2,"Bracken, Mr. James H",male,27,0,0,220367,13,,S
223,0,3,"Green, Mr. George Henry",male,51,0,0,21440,8.05,,S
224,0,3,"Nenkoff, Mr. Christo",male,,0,0,349234,7.8958,,S
225,1,1,"Hoyt, Mr. Frederick Maxfield",male,38,1,0,19943,90,C93,S
226,0,3,"Berglund, Mr. Karl Ivar Sven",male,22,0,0,PP 4348,9.35,,S
227,1,2,"Mellors, Mr. William John",male,19,0,0,SW/PP 751,10.5,,S
228,0,3,"Lovell, Mr. John Hall (""Henry"")",male,20.5,0,0,A/5 21173,7.25,,S
229,0,2,"Fahlstrom, Mr. Arne Jonas",male,18,0,0,236171,13,,S
230,0,3,"Lefebre, Miss. Mathilde",female,,3,1,4133,25.4667,,S
231,1,1,"Harris, Mrs. Henry Birkhardt (Irene Wallach)",female,35,1,0,36973,83.475,C83,S
232,0,3,"Larsson, Mr. Bengt Edvin",male,29,0,0,347067,7.775,,S
233,0,2,"Sjostedt, Mr. Ernst Adolf",male,59,0,0,237442,13.5,,S
234,1,3,"Asplund, Miss. Lillian Gertrud",female,5,4,2,347077,31.3875,,S
235,0,2,"Leyson, Mr. Robert William Norman",male,24,0,0,C.A. 29566,10.5,,S
236,0,3,"Harknett, Miss. Alice Phoebe",female,,0,0,W./C. 6609,7.55,,S
237,0,2,"Hold, Mr. Stephen",male,44,1,0,26707,26,,S
238,1,2,"Collyer, Miss. Marjorie ""Lottie""",female,8,0,2,C.A. 31921,26.25,,S
239,0,2,"Pengelly, Mr. Frederick William",male,19,0,0,28665,10.5,,S
240,0,2,"Hunt, Mr. George Henry",male,33,0,0,SCO/W 1585,12.275,,S
241,0,3,"Zabour, Miss. Thamine",female,,1,0,2665,14.4542,,C
242,1,3,"Murphy, Miss. Katherine ""Kate""",female,,1,0,367230,15.5,,Q
243,0,2,"Coleridge, Mr. Reginald Charles",male,29,0,0,W./C. 14263,10.5,,S
244,0,3,"Maenpaa, Mr. Matti Alexanteri",male,22,0,0,STON/O 2. 3101275,7.125,,S
245,0,3,"Attalah, Mr. Sleiman",male,30,0,0,2694,7.225,,C
246,0,1,"Minahan, Dr. William Edward",male,44,2,0,19928,90,C78,Q
247,0,3,"Lindahl, Miss. Agda Thorilda Viktoria",female,25,0,0,347071,7.775,,S
248,1,2,"Hamalainen, Mrs. William (Anna)",female,24,0,2,250649,14.5,,S
249,1,1,"Beckwith, Mr. Richard Leonard",male,37,1,1,11751,52.5542,D35,S
250,0,2,"Carter, Rev. Ernest Courtenay",male,54,1,0,244252,26,,S
251,0,3,"Reed, Mr. James George",male,,0,0,362316,7.25,,S
252,0,3,"Strom, Mrs. Wilhelm (Elna Matilda Persson)",female,29,1,1,347054,10.4625,G6,S
253,0,1,"Stead, Mr. William Thomas",male,62,0,0,113514,26.55,C87,S
254,0,3,"Lobb, Mr. William Arthur",male,30,1,0,A/5. 3336,16.1,,S
255,0,3,"Rosblom, Mrs. Viktor (Helena Wilhelmina)",female,41,0,2,370129,20.2125,,S
256,1,3,"Touma, Mrs. Darwis (Hanne Youssef Razi)",female,29,0,2,2650,15.2458,,C
257,1,1,"Thorne, Mrs. Gertrude Maybelle",female,,0,0,PC 17585,79.2,,C
258,1,1,"Cherry, Miss. Gladys",female,30,0,0,110152,86.5,B77,S
259,1,1,"Ward, Miss. Anna",female,35,0,0,PC 17755,512.3292,,C
260,1,2,"Parrish, Mrs. (Lutie Davis)",female,50,0,1,230433,26,,S
261,0,3,"Smith, Mr. Thomas",male,,0,0,384461,7.75,,Q
262,1,3,"Asplund, Master. Edvin Rojj Felix",male,3,4,2,347077,31.3875,,S
263,0,1,"Taussig, Mr. Emil",male,52,1,1,110413,79.65,E67,S
264,0,1,"Harrison, Mr. William",male,40,0,0,112059,0,B94,S
265,0,3,"Henry, Miss. Delia",female,,0,0,382649,7.75,,Q
266,0,2,"Reeves, Mr. David",male,36,0,0,C.A. 17248,10.5,,S
267,0,3,"Panula, Mr. Ernesti Arvid",male,16,4,1,3101295,39.6875,,S
268,1,3,"Persson, Mr. Ernst Ulrik",male,25,1,0,347083,7.775,,S
269,1,1,"Graham, Mrs. William Thompson (Edith Junkins)",female,58,0,1,PC 17582,153.4625,C125,S
270,1,1,"Bissette, Miss. Amelia",female,35,0,0,PC 17760,135.6333,C99,S
271,0,1,"Cairns, Mr. Alexander",male,,0,0,113798,31,,S
272,1,3,"Tornquist, Mr. William Henry",male,25,0,0,LINE,0,,S
273,1,2,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41,0,1,250644,19.5,,S
274,0,1,"Natsch, Mr. Charles H",male,37,0,1,PC 17596,29.7,C118,C
275,1,3,"Healy, Miss. Hanora ""Nora""",female,,0,0,370375,7.75,,Q
276,1,1,"Andrews, Miss. Kornelia Theodosia",female,63,1,0,13502,77.9583,D7,S
277,0,3,"Lindblom, Miss. Augusta Charlotta",female,45,0,0,347073,7.75,,S
278,0,2,"Parkes, Mr. Francis ""Frank""",male,,0,0,239853,0,,S
279,0,3,"Rice, Master. Eric",male,7,4,1,382652,29.125,,Q
280,1,3,"Abbott, Mrs. Stanton (Rosa Hunt)",female,35,1,1,C.A. 2673,20.25,,S
281,0,3,"Duane, Mr. Frank",male,65,0,0,336439,7.75,,Q
282,0,3,"Olsson, Mr. Nils Johan Goransson",male,28,0,0,347464,7.8542,,S
283,0,3,"de Pelsmaeker, Mr. Alfons",male,16,0,0,345778,9.5,,S
284,1,3,"Dorking, Mr. Edward Arthur",male,19,0,0,A/5. 10482,8.05,,S
285,0,1,"Smith, Mr. Richard William",male,,0,0,113056,26,A19,S
286,0,3,"Stankovic, Mr. Ivan",male,33,0,0,349239,8.6625,,C
287,1,3,"de Mulder, Mr. Theodore",male,30,0,0,345774,9.5,,S
288,0,3,"Naidenoff, Mr. Penko",male,22,0,0,349206,7.8958,,S
289,1,2,"Hosono, Mr. Masabumi",male,42,0,0,237798,13,,S
290,1,3,"Connolly, Miss. Kate",female,22,0,0,370373,7.75,,Q
291,1,1,"Barber, Miss. Ellen ""Nellie""",female,26,0,0,19877,78.85,,S
292,1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19,1,0,11967,91.0792,B49,C
293,0,2,"Levy, Mr. Rene Jacques",male,36,0,0,SC/Paris 2163,12.875,D,C
294,0,3,"Haas, Miss. Aloisia",female,24,0,0,349236,8.85,,S
295,0,3,"Mineff, Mr. Ivan",male,24,0,0,349233,7.8958,,S
296,0,1,"Lewy, Mr. Ervin G",male,,0,0,PC 17612,27.7208,,C
297,0,3,"Hanna, Mr. Mansour",male,23.5,0,0,2693,7.2292,,C
298,0,1,"Allison, Miss. Helen Loraine",female,2,1,2,113781,151.55,C22 C26,S
299,1,1,"Saalfeld, Mr. Adolphe",male,,0,0,19988,30.5,C106,S
300,1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50,0,1,PC 17558,247.5208,B58 B60,C
301,1,3,"Kelly, Miss. Anna Katherine ""Annie Kate""",female,,0,0,9234,7.75,,Q
302,1,3,"McCoy, Mr. Bernard",male,,2,0,367226,23.25,,Q
303,0,3,"Johnson, Mr. William Cahoone Jr",male,19,0,0,LINE,0,,S
304,1,2,"Keane, Miss. Nora A",female,,0,0,226593,12.35,E101,Q
305,0,3,"Williams, Mr. Howard Hugh ""Harry""",male,,0,0,A/5 2466,8.05,,S
306,1,1,"Allison, Master. Hudson Trevor",male,0.92,1,2,113781,151.55,C22 C26,S
307,1,1,"Fleming, Miss. Margaret",female,,0,0,17421,110.8833,,C
308,1,1,"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)",female,17,1,0,PC 17758,108.9,C65,C
309,0,2,"Abelson, Mr. Samuel",male,30,1,0,P/PP 3381,24,,C
310,1,1,"Francatelli, Miss. Laura Mabel",female,30,0,0,PC 17485,56.9292,E36,C
311,1,1,"Hays, Miss. Margaret Bechstein",female,24,0,0,11767,83.1583,C54,C
312,1,1,"Ryerson, Miss. Emily Borie",female,18,2,2,PC 17608,262.375,B57 B59 B63 B66,C
313,0,2,"Lahtinen, Mrs. William (Anna Sylfven)",female,26,1,1,250651,26,,S
314,0,3,"Hendekovic, Mr. Ignjac",male,28,0,0,349243,7.8958,,S
315,0,2,"Hart, Mr. Benjamin",male,43,1,1,F.C.C. 13529,26.25,,S
316,1,3,"Nilsson, Miss. Helmina Josefina",female,26,0,0,347470,7.8542,,S
317,1,2,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24,1,0,244367,26,,S
318,0,2,"Moraweck, Dr. Ernest",male,54,0,0,29011,14,,S
319,1,1,"Wick, Miss. Mary Natalie",female,31,0,2,36928,164.8667,C7,S
320,1,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",female,40,1,1,16966,134.5,E34,C
321,0,3,"Dennis, Mr. Samuel",male,22,0,0,A/5 21172,7.25,,S
322,0,3,"Danoff, Mr. Yoto",male,27,0,0,349219,7.8958,,S
323,1,2,"Slayter, Miss. Hilda Mary",female,30,0,0,234818,12.35,,Q
324,1,2,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",female,22,1,1,248738,29,,S
325,0,3,"Sage, Mr. George John Jr",male,,8,2,CA. 2343,69.55,,S
326,1,1,"Young, Miss. Marie Grice",female,36,0,0,PC 17760,135.6333,C32,C
327,0,3,"Nysveen, Mr. Johan Hansen",male,61,0,0,345364,6.2375,,S
328,1,2,"Ball, Mrs. (Ada E Hall)",female,36,0,0,28551,13,D,S
329,1,3,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",female,31,1,1,363291,20.525,,S
330,1,1,"Hippach, Miss. Jean Gertrude",female,16,0,1,111361,57.9792,B18,C
331,1,3,"McCoy, Miss. Agnes",female,,2,0,367226,23.25,,Q
332,0,1,"Partner, Mr. Austen",male,45.5,0,0,113043,28.5,C124,S
333,0,1,"Graham, Mr. George Edward",male,38,0,1,PC 17582,153.4625,C91,S
334,0,3,"Vander Planke, Mr. Leo Edmondus",male,16,2,0,345764,18,,S
335,1,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",female,,1,0,PC 17611,133.65,,S
336,0,3,"Denkoff, Mr. Mitto",male,,0,0,349225,7.8958,,S
337,0,1,"Pears, Mr. Thomas Clinton",male,29,1,0,113776,66.6,C2,S
338,1,1,"Burns, Miss. Elizabeth Margaret",female,41,0,0,16966,134.5,E40,C
339,1,3,"Dahl, Mr. Karl Edwart",male,45,0,0,7598,8.05,,S
340,0,1,"Blackwell, Mr. Stephen Weart",male,45,0,0,113784,35.5,T,S
341,1,2,"Navratil, Master. Edmond Roger",male,2,1,1,230080,26,F2,S
342,1,1,"Fortune, Miss. Alice Elizabeth",female,24,3,2,19950,263,C23 C25 C27,S
343,0,2,"Collander, Mr. Erik Gustaf",male,28,0,0,248740,13,,S
344,0,2,"Sedgwick, Mr. Charles Frederick Waddington",male,25,0,0,244361,13,,S
345,0,2,"Fox, Mr. Stanley Hubert",male,36,0,0,229236,13,,S
346,1,2,"Brown, Miss. Amelia ""Mildred""",female,24,0,0,248733,13,F33,S
347,1,2,"Smith, Miss. Marion Elsie",female,40,0,0,31418,13,,S
348,1,3,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S
349,1,3,"Coutts, Master. William Loch ""William""",male,3,1,1,C.A. 37671,15.9,,S
350,0,3,"Dimic, Mr. Jovan",male,42,0,0,315088,8.6625,,S
351,0,3,"Odahl, Mr. Nils Martin",male,23,0,0,7267,9.225,,S
352,0,1,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35,C128,S
353,0,3,"Elias, Mr. Tannous",male,15,1,1,2695,7.2292,,C
354,0,3,"Arnold-Franchi, Mr. Josef",male,25,1,0,349237,17.8,,S
355,0,3,"Yousif, Mr. Wazli",male,,0,0,2647,7.225,,C
356,0,3,"Vanden Steen, Mr. Leo Peter",male,28,0,0,345783,9.5,,S
357,1,1,"Bowerman, Miss. Elsie Edith",female,22,0,1,113505,55,E33,S
358,0,2,"Funk, Miss. Annie Clemmer",female,38,0,0,237671,13,,S
359,1,3,"McGovern, Miss. Mary",female,,0,0,330931,7.8792,,Q
360,1,3,"Mockler, Miss. Helen Mary ""Ellie""",female,,0,0,330980,7.8792,,Q
361,0,3,"Skoog, Mr. Wilhelm",male,40,1,4,347088,27.9,,S
362,0,2,"del Carlo, Mr. Sebastiano",male,29,1,0,SC/PARIS 2167,27.7208,,C
363,0,3,"Barbara, Mrs. (Catherine David)",female,45,0,1,2691,14.4542,,C
364,0,3,"Asim, Mr. Adola",male,35,0,0,SOTON/O.Q. 3101310,7.05,,S
365,0,3,"O'Brien, Mr. Thomas",male,,1,0,370365,15.5,,Q
366,0,3,"Adahl, Mr. Mauritz Nils Martin",male,30,0,0,C 7076,7.25,,S
367,1,1,"Warren, Mrs. Frank Manley (Anna Sophia Atkinson)",female,60,1,0,110813,75.25,D37,C
368,1,3,"Moussa, Mrs. (Mantoura Boulos)",female,,0,0,2626,7.2292,,C
369,1,3,"Jermyn, Miss. Annie",female,,0,0,14313,7.75,,Q
370,1,1,"Aubart, Mme. Leontine Pauline",female,24,0,0,PC 17477,69.3,B35,C
371,1,1,"Harder, Mr. George Achilles",male,25,1,0,11765,55.4417,E50,C
372,0,3,"Wiklund, Mr. Jakob Alfred",male,18,1,0,3101267,6.4958,,S
373,0,3,"Beavan, Mr. William Thomas",male,19,0,0,323951,8.05,,S
374,0,1,"Ringhini, Mr. Sante",male,22,0,0,PC 17760,135.6333,,C
375,0,3,"Palsson, Miss. Stina Viola",female,3,3,1,349909,21.075,,S
376,1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C
377,1,3,"Landergren, Miss. Aurora Adelia",female,22,0,0,C 7077,7.25,,S
378,0,1,"Widener, Mr. Harry Elkins",male,27,0,2,113503,211.5,C82,C
379,0,3,"Betros, Mr. Tannous",male,20,0,0,2648,4.0125,,C
380,0,3,"Gustafsson, Mr. Karl Gideon",male,19,0,0,347069,7.775,,S
381,1,1,"Bidois, Miss. Rosalie",female,42,0,0,PC 17757,227.525,,C
382,1,3,"Nakid, Miss. Maria (""Mary"")",female,1,0,2,2653,15.7417,,C
383,0,3,"Tikkanen, Mr. Juho",male,32,0,0,STON/O 2. 3101293,7.925,,S
384,1,1,"Holverson, Mrs. Alexander Oskar (Mary Aline Towner)",female,35,1,0,113789,52,,S
385,0,3,"Plotcharsky, Mr. Vasil",male,,0,0,349227,7.8958,,S
386,0,2,"Davies, Mr. Charles Henry",male,18,0,0,S.O.C. 14879,73.5,,S
387,0,3,"Goodwin, Master. Sidney Leonard",male,1,5,2,CA 2144,46.9,,S
388,1,2,"Buss, Miss. Kate",female,36,0,0,27849,13,,S
389,0,3,"Sadlier, Mr. Matthew",male,,0,0,367655,7.7292,,Q
390,1,2,"Lehmann, Miss. Bertha",female,17,0,0,SC 1748,12,,C
391,1,1,"Carter, Mr. William Ernest",male,36,1,2,113760,120,B96 B98,S
392,1,3,"Jansson, Mr. Carl Olof",male,21,0,0,350034,7.7958,,S
393,0,3,"Gustafsson, Mr. Johan Birger",male,28,2,0,3101277,7.925,,S
394,1,1,"Newell, Miss. Marjorie",female,23,1,0,35273,113.275,D36,C
395,1,3,"Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)",female,24,0,2,PP 9549,16.7,G6,S
396,0,3,"Johansson, Mr. Erik",male,22,0,0,350052,7.7958,,S
397,0,3,"Olsson, Miss. Elina",female,31,0,0,350407,7.8542,,S
398,0,2,"McKane, Mr. Peter David",male,46,0,0,28403,26,,S
399,0,2,"Pain, Dr. Alfred",male,23,0,0,244278,10.5,,S
400,1,2,"Trout, Mrs. William H (Jessie L)",female,28,0,0,240929,12.65,,S
401,1,3,"Niskanen, Mr. Juha",male,39,0,0,STON/O 2. 3101289,7.925,,S
402,0,3,"Adams, Mr. John",male,26,0,0,341826,8.05,,S
403,0,3,"Jussila, Miss. Mari Aina",female,21,1,0,4137,9.825,,S
404,0,3,"Hakkarainen, Mr. Pekka Pietari",male,28,1,0,STON/O2. 3101279,15.85,,S
405,0,3,"Oreskovic, Miss. Marija",female,20,0,0,315096,8.6625,,S
406,0,2,"Gale, Mr. Shadrach",male,34,1,0,28664,21,,S
407,0,3,"Widegren, Mr. Carl/Charles Peter",male,51,0,0,347064,7.75,,S
408,1,2,"Richards, Master. William Rowe",male,3,1,1,29106,18.75,,S
409,0,3,"Birkeland, Mr. Hans Martin Monsen",male,21,0,0,312992,7.775,,S
410,0,3,"Lefebre, Miss. Ida",female,,3,1,4133,25.4667,,S
411,0,3,"Sdycoff, Mr. Todor",male,,0,0,349222,7.8958,,S
412,0,3,"Hart, Mr. Henry",male,,0,0,394140,6.8583,,Q
413,1,1,"Minahan, Miss. Daisy E",female,33,1,0,19928,90,C78,Q
414,0,2,"Cunningham, Mr. Alfred Fleming",male,,0,0,239853,0,,S
415,1,3,"Sundman, Mr. Johan Julian",male,44,0,0,STON/O 2. 3101269,7.925,,S
416,0,3,"Meek, Mrs. Thomas (Annie Louise Rowley)",female,,0,0,343095,8.05,,S
417,1,2,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34,1,1,28220,32.5,,S
418,1,2,"Silven, Miss. Lyyli Karoliina",female,18,0,2,250652,13,,S
419,0,2,"Matthews, Mr. William John",male,30,0,0,28228,13,,S
420,0,3,"Van Impe, Miss. Catharina",female,10,0,2,345773,24.15,,S
421,0,3,"Gheorgheff, Mr. Stanio",male,,0,0,349254,7.8958,,C
422,0,3,"Charters, Mr. David",male,21,0,0,A/5. 13032,7.7333,,Q
423,0,3,"Zimmerman, Mr. Leo",male,29,0,0,315082,7.875,,S
424,0,3,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28,1,1,347080,14.4,,S
425,0,3,"Rosblom, Mr. Viktor Richard",male,18,1,1,370129,20.2125,,S
426,0,3,"Wiseman, Mr. Phillippe",male,,0,0,A/4. 34244,7.25,,S
427,1,2,"Clarke, Mrs. Charles V (Ada Maria Winfield)",female,28,1,0,2003,26,,S
428,1,2,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",female,19,0,0,250655,26,,S
429,0,3,"Flynn, Mr. James",male,,0,0,364851,7.75,,Q
430,1,3,"Pickard, Mr. Berk (Berk Trembisky)",male,32,0,0,SOTON/O.Q. 392078,8.05,E10,S
431,1,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan",male,28,0,0,110564,26.55,C52,S
432,1,3,"Thorneycroft, Mrs. Percival (Florence Kate White)",female,,1,0,376564,16.1,,S
433,1,2,"Louch, Mrs. Charles Alexander (Alice Adelaide Slow)",female,42,1,0,SC/AH 3085,26,,S
434,0,3,"Kallio, Mr. Nikolai Erland",male,17,0,0,STON/O 2. 3101274,7.125,,S
435,0,1,"Silvey, Mr. William Baird",male,50,1,0,13507,55.9,E44,S
436,1,1,"Carter, Miss. Lucile Polk",female,14,1,2,113760,120,B96 B98,S
437,0,3,"Ford, Miss. Doolina Margaret ""Daisy""",female,21,2,2,W./C. 6608,34.375,,S
438,1,2,"Richards, Mrs. Sidney (Emily Hocking)",female,24,2,3,29106,18.75,,S
439,0,1,"Fortune, Mr. Mark",male,64,1,4,19950,263,C23 C25 C27,S
440,0,2,"Kvillner, Mr. Johan Henrik Johannesson",male,31,0,0,C.A. 18723,10.5,,S
441,1,2,"Hart, Mrs. Benjamin (Esther Ada Bloomfield)",female,45,1,1,F.C.C. 13529,26.25,,S
442,0,3,"Hampe, Mr. Leon",male,20,0,0,345769,9.5,,S
443,0,3,"Petterson, Mr. Johan Emil",male,25,1,0,347076,7.775,,S
444,1,2,"Reynaldo, Ms. Encarnacion",female,28,0,0,230434,13,,S
445,1,3,"Johannesen-Bratthammer, Mr. Bernt",male,,0,0,65306,8.1125,,S
446,1,1,"Dodge, Master. Washington",male,4,0,2,33638,81.8583,A34,S
447,1,2,"Mellinger, Miss. Madeleine Violet",female,13,0,1,250644,19.5,,S
448,1,1,"Seward, Mr. Frederic Kimber",male,34,0,0,113794,26.55,,S
449,1,3,"Baclini, Miss. Marie Catherine",female,5,2,1,2666,19.2583,,C
450,1,1,"Peuchen, Major. Arthur Godfrey",male,52,0,0,113786,30.5,C104,S
451,0,2,"West, Mr. Edwy Arthur",male,36,1,2,C.A. 34651,27.75,,S
452,0,3,"Hagland, Mr. Ingvald Olai Olsen",male,,1,0,65303,19.9667,,S
453,0,1,"Foreman, Mr. Benjamin Laventall",male,30,0,0,113051,27.75,C111,C
454,1,1,"Goldenberg, Mr. Samuel L",male,49,1,0,17453,89.1042,C92,C
455,0,3,"Peduzzi, Mr. Joseph",male,,0,0,A/5 2817,8.05,,S
456,1,3,"Jalsevac, Mr. Ivan",male,29,0,0,349240,7.8958,,C
457,0,1,"Millet, Mr. Francis Davis",male,65,0,0,13509,26.55,E38,S
458,1,1,"Kenyon, Mrs. Frederick R (Marion)",female,,1,0,17464,51.8625,D21,S
459,1,2,"Toomey, Miss. Ellen",female,50,0,0,F.C.C. 13531,10.5,,S
460,0,3,"O'Connor, Mr. Maurice",male,,0,0,371060,7.75,,Q
461,1,1,"Anderson, Mr. Harry",male,48,0,0,19952,26.55,E12,S
462,0,3,"Morley, Mr. William",male,34,0,0,364506,8.05,,S
463,0,1,"Gee, Mr. Arthur H",male,47,0,0,111320,38.5,E63,S
464,0,2,"Milling, Mr. Jacob Christian",male,48,0,0,234360,13,,S
465,0,3,"Maisner, Mr. Simon",male,,0,0,A/S 2816,8.05,,S
466,0,3,"Goncalves, Mr. Manuel Estanslas",male,38,0,0,SOTON/O.Q. 3101306,7.05,,S
467,0,2,"Campbell, Mr. William",male,,0,0,239853,0,,S
468,0,1,"Smart, Mr. John Montgomery",male,56,0,0,113792,26.55,,S
469,0,3,"Scanlan, Mr. James",male,,0,0,36209,7.725,,Q
470,1,3,"Baclini, Miss. Helene Barbara",female,0.75,2,1,2666,19.2583,,C
471,0,3,"Keefe, Mr. Arthur",male,,0,0,323592,7.25,,S
472,0,3,"Cacic, Mr. Luka",male,38,0,0,315089,8.6625,,S
473,1,2,"West, Mrs. Edwy Arthur (Ada Mary Worth)",female,33,1,2,C.A. 34651,27.75,,S
474,1,2,"Jerwan, Mrs. Amin S (Marie Marthe Thuillard)",female,23,0,0,SC/AH Basle 541,13.7917,D,C
475,0,3,"Strandberg, Miss. Ida Sofia",female,22,0,0,7553,9.8375,,S
476,0,1,"Clifford, Mr. George Quincy",male,,0,0,110465,52,A14,S
477,0,2,"Renouf, Mr. Peter Henry",male,34,1,0,31027,21,,S
478,0,3,"Braund, Mr. Lewis Richard",male,29,1,0,3460,7.0458,,S
479,0,3,"Karlsson, Mr. Nils August",male,22,0,0,350060,7.5208,,S
480,1,3,"Hirvonen, Miss. Hildur E",female,2,0,1,3101298,12.2875,,S
481,0,3,"Goodwin, Master. Harold Victor",male,9,5,2,CA 2144,46.9,,S
482,0,2,"Frost, Mr. Anthony Wood ""Archie""",male,,0,0,239854,0,,S
483,0,3,"Rouse, Mr. Richard Henry",male,50,0,0,A/5 3594,8.05,,S
484,1,3,"Turkula, Mrs. (Hedwig)",female,63,0,0,4134,9.5875,,S
485,1,1,"Bishop, Mr. Dickinson H",male,25,1,0,11967,91.0792,B49,C
486,0,3,"Lefebre, Miss. Jeannie",female,,3,1,4133,25.4667,,S
487,1,1,"Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)",female,35,1,0,19943,90,C93,S
488,0,1,"Kent, Mr. Edward Austin",male,58,0,0,11771,29.7,B37,C
489,0,3,"Somerton, Mr. Francis William",male,30,0,0,A.5. 18509,8.05,,S
490,1,3,"Coutts, Master. Eden Leslie ""Neville""",male,9,1,1,C.A. 37671,15.9,,S
491,0,3,"Hagland, Mr. Konrad Mathias Reiersen",male,,1,0,65304,19.9667,,S
492,0,3,"Windelov, Mr. Einar",male,21,0,0,SOTON/OQ 3101317,7.25,,S
493,0,1,"Molson, Mr. Harry Markland",male,55,0,0,113787,30.5,C30,S
494,0,1,"Artagaveytia, Mr. Ramon",male,71,0,0,PC 17609,49.5042,,C
495,0,3,"Stanley, Mr. Edward Roland",male,21,0,0,A/4 45380,8.05,,S
496,0,3,"Yousseff, Mr. Gerious",male,,0,0,2627,14.4583,,C
497,1,1,"Eustis, Miss. Elizabeth Mussey",female,54,1,0,36947,78.2667,D20,C
498,0,3,"Shellard, Mr. Frederick William",male,,0,0,C.A. 6212,15.1,,S
499,0,1,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)",female,25,1,2,113781,151.55,C22 C26,S
500,0,3,"Svensson, Mr. Olof",male,24,0,0,350035,7.7958,,S
501,0,3,"Calic, Mr. Petar",male,17,0,0,315086,8.6625,,S
502,0,3,"Canavan, Miss. Mary",female,21,0,0,364846,7.75,,Q
503,0,3,"O'Sullivan, Miss. Bridget Mary",female,,0,0,330909,7.6292,,Q
504,0,3,"Laitinen, Miss. Kristina Sofia",female,37,0,0,4135,9.5875,,S
505,1,1,"Maioni, Miss. Roberta",female,16,0,0,110152,86.5,B79,S
506,0,1,"Penasco y Castellana, Mr. Victor de Satode",male,18,1,0,PC 17758,108.9,C65,C
507,1,2,"Quick, Mrs. Frederick Charles (Jane Richards)",female,33,0,2,26360,26,,S
508,1,1,"Bradley, Mr. George (""George Arthur Brayton"")",male,,0,0,111427,26.55,,S
509,0,3,"Olsen, Mr. Henry Margido",male,28,0,0,C 4001,22.525,,S
510,1,3,"Lang, Mr. Fang",male,26,0,0,1601,56.4958,,S
511,1,3,"Daly, Mr. Eugene Patrick",male,29,0,0,382651,7.75,,Q
512,0,3,"Webber, Mr. James",male,,0,0,SOTON/OQ 3101316,8.05,,S
513,1,1,"McGough, Mr. James Robert",male,36,0,0,PC 17473,26.2875,E25,S
514,1,1,"Rothschild, Mrs. Martin (Elizabeth L. Barrett)",female,54,1,0,PC 17603,59.4,,C
515,0,3,"Coleff, Mr. Satio",male,24,0,0,349209,7.4958,,S
516,0,1,"Walker, Mr. William Anderson",male,47,0,0,36967,34.0208,D46,S
517,1,2,"Lemore, Mrs. (Amelia Milley)",female,34,0,0,C.A. 34260,10.5,F33,S
518,0,3,"Ryan, Mr. Patrick",male,,0,0,371110,24.15,,Q
519,1,2,"Angle, Mrs. William A (Florence ""Mary"" Agnes Hughes)",female,36,1,0,226875,26,,S
520,0,3,"Pavlovic, Mr. Stefo",male,32,0,0,349242,7.8958,,S
521,1,1,"Perreault, Miss. Anne",female,30,0,0,12749,93.5,B73,S
522,0,3,"Vovk, Mr. Janko",male,22,0,0,349252,7.8958,,S
523,0,3,"Lahoud, Mr. Sarkis",male,,0,0,2624,7.225,,C
524,1,1,"Hippach, Mrs. Louis Albert (Ida Sophia Fischer)",female,44,0,1,111361,57.9792,B18,C
525,0,3,"Kassem, Mr. Fared",male,,0,0,2700,7.2292,,C
526,0,3,"Farrell, Mr. James",male,40.5,0,0,367232,7.75,,Q
527,1,2,"Ridsdale, Miss. Lucy",female,50,0,0,W./C. 14258,10.5,,S
528,0,1,"Farthing, Mr. John",male,,0,0,PC 17483,221.7792,C95,S
529,0,3,"Salonen, Mr. Johan Werner",male,39,0,0,3101296,7.925,,S
530,0,2,"Hocking, Mr. Richard George",male,23,2,1,29104,11.5,,S
531,1,2,"Quick, Miss. Phyllis May",female,2,1,1,26360,26,,S
532,0,3,"Toufik, Mr. Nakli",male,,0,0,2641,7.2292,,C
533,0,3,"Elias, Mr. Joseph Jr",male,17,1,1,2690,7.2292,,C
534,1,3,"Peter, Mrs. Catherine (Catherine Rizk)",female,,0,2,2668,22.3583,,C
535,0,3,"Cacic, Miss. Marija",female,30,0,0,315084,8.6625,,S
536,1,2,"Hart, Miss. Eva Miriam",female,7,0,2,F.C.C. 13529,26.25,,S
537,0,1,"Butt, Major. Archibald Willingham",male,45,0,0,113050,26.55,B38,S
538,1,1,"LeRoy, Miss. Bertha",female,30,0,0,PC 17761,106.425,,C
539,0,3,"Risien, Mr. Samuel Beard",male,,0,0,364498,14.5,,S
540,1,1,"Frolicher, Miss. Hedwig Margaritha",female,22,0,2,13568,49.5,B39,C
541,1,1,"Crosby, Miss. Harriet R",female,36,0,2,WE/P 5735,71,B22,S
542,0,3,"Andersson, Miss. Ingeborg Constanzia",female,9,4,2,347082,31.275,,S
543,0,3,"Andersson, Miss. Sigrid Elisabeth",female,11,4,2,347082,31.275,,S
544,1,2,"Beane, Mr. Edward",male,32,1,0,2908,26,,S
545,0,1,"Douglas, Mr. Walter Donald",male,50,1,0,PC 17761,106.425,C86,C
546,0,1,"Nicholson, Mr. Arthur Ernest",male,64,0,0,693,26,,S
547,1,2,"Beane, Mrs. Edward (Ethel Clarke)",female,19,1,0,2908,26,,S
548,1,2,"Padro y Manent, Mr. Julian",male,,0,0,SC/PARIS 2146,13.8625,,C
549,0,3,"Goldsmith, Mr. Frank John",male,33,1,1,363291,20.525,,S
550,1,2,"Davies, Master. John Morgan Jr",male,8,1,1,C.A. 33112,36.75,,S
551,1,1,"Thayer, Mr. John Borland Jr",male,17,0,2,17421,110.8833,C70,C
552,0,2,"Sharp, Mr. Percival James R",male,27,0,0,244358,26,,S
553,0,3,"O'Brien, Mr. Timothy",male,,0,0,330979,7.8292,,Q
554,1,3,"Leeni, Mr. Fahim (""Philip Zenni"")",male,22,0,0,2620,7.225,,C
555,1,3,"Ohman, Miss. Velin",female,22,0,0,347085,7.775,,S
556,0,1,"Wright, Mr. George",male,62,0,0,113807,26.55,,S
557,1,1,"Duff Gordon, Lady. (Lucille Christiana Sutherland) (""Mrs Morgan"")",female,48,1,0,11755,39.6,A16,C
558,0,1,"Robbins, Mr. Victor",male,,0,0,PC 17757,227.525,,C
559,1,1,"Taussig, Mrs. Emil (Tillie Mandelbaum)",female,39,1,1,110413,79.65,E67,S
560,1,3,"de Messemaeker, Mrs. Guillaume Joseph (Emma)",female,36,1,0,345572,17.4,,S
561,0,3,"Morrow, Mr. Thomas Rowan",male,,0,0,372622,7.75,,Q
562,0,3,"Sivic, Mr. Husein",male,40,0,0,349251,7.8958,,S
563,0,2,"Norman, Mr. Robert Douglas",male,28,0,0,218629,13.5,,S
564,0,3,"Simmons, Mr. John",male,,0,0,SOTON/OQ 392082,8.05,,S
565,0,3,"Meanwell, Miss. (Marion Ogden)",female,,0,0,SOTON/O.Q. 392087,8.05,,S
566,0,3,"Davies, Mr. Alfred J",male,24,2,0,A/4 48871,24.15,,S
567,0,3,"Stoytcheff, Mr. Ilia",male,19,0,0,349205,7.8958,,S
568,0,3,"Palsson, Mrs. Nils (Alma Cornelia Berglund)",female,29,0,4,349909,21.075,,S
569,0,3,"Doharr, Mr. Tannous",male,,0,0,2686,7.2292,,C
570,1,3,"Jonsson, Mr. Carl",male,32,0,0,350417,7.8542,,S
571,1,2,"Harris, Mr. George",male,62,0,0,S.W./PP 752,10.5,,S
572,1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)",female,53,2,0,11769,51.4792,C101,S
573,1,1,"Flynn, Mr. John Irwin (""Irving"")",male,36,0,0,PC 17474,26.3875,E25,S
574,1,3,"Kelly, Miss. Mary",female,,0,0,14312,7.75,,Q
575,0,3,"Rush, Mr. Alfred George John",male,16,0,0,A/4. 20589,8.05,,S
576,0,3,"Patchett, Mr. George",male,19,0,0,358585,14.5,,S
577,1,2,"Garside, Miss. Ethel",female,34,0,0,243880,13,,S
578,1,1,"Silvey, Mrs. William Baird (Alice Munger)",female,39,1,0,13507,55.9,E44,S
579,0,3,"Caram, Mrs. Joseph (Maria Elias)",female,,1,0,2689,14.4583,,C
580,1,3,"Jussila, Mr. Eiriik",male,32,0,0,STON/O 2. 3101286,7.925,,S
581,1,2,"Christy, Miss. Julie Rachel",female,25,1,1,237789,30,,S
582,1,1,"Thayer, Mrs. John Borland (Marian Longstreth Morris)",female,39,1,1,17421,110.8833,C68,C
583,0,2,"Downton, Mr. William James",male,54,0,0,28403,26,,S
584,0,1,"Ross, Mr. John Hugo",male,36,0,0,13049,40.125,A10,C
585,0,3,"Paulner, Mr. Uscher",male,,0,0,3411,8.7125,,C
586,1,1,"Taussig, Miss. Ruth",female,18,0,2,110413,79.65,E68,S
587,0,2,"Jarvis, Mr. John Denzil",male,47,0,0,237565,15,,S
588,1,1,"Frolicher-Stehli, Mr. Maxmillian",male,60,1,1,13567,79.2,B41,C
589,0,3,"Gilinski, Mr. Eliezer",male,22,0,0,14973,8.05,,S
590,0,3,"Murdlin, Mr. Joseph",male,,0,0,A./5. 3235,8.05,,S
591,0,3,"Rintamaki, Mr. Matti",male,35,0,0,STON/O 2. 3101273,7.125,,S
592,1,1,"Stephenson, Mrs. Walter Bertram (Martha Eustis)",female,52,1,0,36947,78.2667,D20,C
593,0,3,"Elsbury, Mr. William James",male,47,0,0,A/5 3902,7.25,,S
594,0,3,"Bourke, Miss. Mary",female,,0,2,364848,7.75,,Q
595,0,2,"Chapman, Mr. John Henry",male,37,1,0,SC/AH 29037,26,,S
596,0,3,"Van Impe, Mr. Jean Baptiste",male,36,1,1,345773,24.15,,S
597,1,2,"Leitch, Miss. Jessie Wills",female,,0,0,248727,33,,S
598,0,3,"Johnson, Mr. Alfred",male,49,0,0,LINE,0,,S
599,0,3,"Boulos, Mr. Hanna",male,,0,0,2664,7.225,,C
600,1,1,"Duff Gordon, Sir. Cosmo Edmund (""Mr Morgan"")",male,49,1,0,PC 17485,56.9292,A20,C
601,1,2,"Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)",female,24,2,1,243847,27,,S
602,0,3,"Slabenoff, Mr. Petco",male,,0,0,349214,7.8958,,S
603,0,1,"Harrington, Mr. Charles H",male,,0,0,113796,42.4,,S
604,0,3,"Torber, Mr. Ernst William",male,44,0,0,364511,8.05,,S
605,1,1,"Homer, Mr. Harry (""Mr E Haven"")",male,35,0,0,111426,26.55,,C
606,0,3,"Lindell, Mr. Edvard Bengtsson",male,36,1,0,349910,15.55,,S
607,0,3,"Karaic, Mr. Milan",male,30,0,0,349246,7.8958,,S
608,1,1,"Daniel, Mr. Robert Williams",male,27,0,0,113804,30.5,,S
609,1,2,"Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)",female,22,1,2,SC/Paris 2123,41.5792,,C
610,1,1,"Shutes, Miss. Elizabeth W",female,40,0,0,PC 17582,153.4625,C125,S
611,0,3,"Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)",female,39,1,5,347082,31.275,,S
612,0,3,"Jardin, Mr. Jose Neto",male,,0,0,SOTON/O.Q. 3101305,7.05,,S
613,1,3,"Murphy, Miss. Margaret Jane",female,,1,0,367230,15.5,,Q
614,0,3,"Horgan, Mr. John",male,,0,0,370377,7.75,,Q
615,0,3,"Brocklebank, Mr. William Alfred",male,35,0,0,364512,8.05,,S
616,1,2,"Herman, Miss. Alice",female,24,1,2,220845,65,,S
617,0,3,"Danbom, Mr. Ernst Gilbert",male,34,1,1,347080,14.4,,S
618,0,3,"Lobb, Mrs. William Arthur (Cordelia K Stanlick)",female,26,1,0,A/5. 3336,16.1,,S
619,1,2,"Becker, Miss. Marion Louise",female,4,2,1,230136,39,F4,S
620,0,2,"Gavey, Mr. Lawrence",male,26,0,0,31028,10.5,,S
621,0,3,"Yasbeck, Mr. Antoni",male,27,1,0,2659,14.4542,,C
622,1,1,"Kimball, Mr. Edwin Nelson Jr",male,42,1,0,11753,52.5542,D19,S
623,1,3,"Nakid, Mr. Sahid",male,20,1,1,2653,15.7417,,C
624,0,3,"Hansen, Mr. Henry Damsgaard",male,21,0,0,350029,7.8542,,S
625,0,3,"Bowen, Mr. David John ""Dai""",male,21,0,0,54636,16.1,,S
626,0,1,"Sutton, Mr. Frederick",male,61,0,0,36963,32.3208,D50,S
627,0,2,"Kirkland, Rev. Charles Leonard",male,57,0,0,219533,12.35,,Q
628,1,1,"Longley, Miss. Gretchen Fiske",female,21,0,0,13502,77.9583,D9,S
629,0,3,"Bostandyeff, Mr. Guentcho",male,26,0,0,349224,7.8958,,S
630,0,3,"O'Connell, Mr. Patrick D",male,,0,0,334912,7.7333,,Q
631,1,1,"Barkworth, Mr. Algernon Henry Wilson",male,80,0,0,27042,30,A23,S
632,0,3,"Lundahl, Mr. Johan Svensson",male,51,0,0,347743,7.0542,,S
633,1,1,"Stahelin-Maeglin, Dr. Max",male,32,0,0,13214,30.5,B50,C
634,0,1,"Parr, Mr. William Henry Marsh",male,,0,0,112052,0,,S
635,0,3,"Skoog, Miss. Mabel",female,9,3,2,347088,27.9,,S
636,1,2,"Davis, Miss. Mary",female,28,0,0,237668,13,,S
637,0,3,"Leinonen, Mr. Antti Gustaf",male,32,0,0,STON/O 2. 3101292,7.925,,S
638,0,2,"Collyer, Mr. Harvey",male,31,1,1,C.A. 31921,26.25,,S
639,0,3,"Panula, Mrs. Juha (Maria Emilia Ojala)",female,41,0,5,3101295,39.6875,,S
640,0,3,"Thorneycroft, Mr. Percival",male,,1,0,376564,16.1,,S
641,0,3,"Jensen, Mr. Hans Peder",male,20,0,0,350050,7.8542,,S
642,1,1,"Sagesser, Mlle. Emma",female,24,0,0,PC 17477,69.3,B35,C
643,0,3,"Skoog, Miss. Margit Elizabeth",female,2,3,2,347088,27.9,,S
644,1,3,"Foo, Mr. Choong",male,,0,0,1601,56.4958,,S
645,1,3,"Baclini, Miss. Eugenie",female,0.75,2,1,2666,19.2583,,C
646,1,1,"Harper, Mr. Henry Sleeper",male,48,1,0,PC 17572,76.7292,D33,C
647,0,3,"Cor, Mr. Liudevit",male,19,0,0,349231,7.8958,,S
648,1,1,"Simonius-Blumer, Col. Oberst Alfons",male,56,0,0,13213,35.5,A26,C
649,0,3,"Willey, Mr. Edward",male,,0,0,S.O./P.P. 751,7.55,,S
650,1,3,"Stanley, Miss. Amy Zillah Elsie",female,23,0,0,CA. 2314,7.55,,S
651,0,3,"Mitkoff, Mr. Mito",male,,0,0,349221,7.8958,,S
652,1,2,"Doling, Miss. Elsie",female,18,0,1,231919,23,,S
653,0,3,"Kalvik, Mr. Johannes Halvorsen",male,21,0,0,8475,8.4333,,S
654,1,3,"O'Leary, Miss. Hanora ""Norah""",female,,0,0,330919,7.8292,,Q
655,0,3,"Hegarty, Miss. Hanora ""Nora""",female,18,0,0,365226,6.75,,Q
656,0,2,"Hickman, Mr. Leonard Mark",male,24,2,0,S.O.C. 14879,73.5,,S
657,0,3,"Radeff, Mr. Alexander",male,,0,0,349223,7.8958,,S
658,0,3,"Bourke, Mrs. John (Catherine)",female,32,1,1,364849,15.5,,Q
659,0,2,"Eitemiller, Mr. George Floyd",male,23,0,0,29751,13,,S
660,0,1,"Newell, Mr. Arthur Webster",male,58,0,2,35273,113.275,D48,C
661,1,1,"Frauenthal, Dr. Henry William",male,50,2,0,PC 17611,133.65,,S
662,0,3,"Badt, Mr. Mohamed",male,40,0,0,2623,7.225,,C
663,0,1,"Colley, Mr. Edward Pomeroy",male,47,0,0,5727,25.5875,E58,S
664,0,3,"Coleff, Mr. Peju",male,36,0,0,349210,7.4958,,S
665,1,3,"Lindqvist, Mr. Eino William",male,20,1,0,STON/O 2. 3101285,7.925,,S
666,0,2,"Hickman, Mr. Lewis",male,32,2,0,S.O.C. 14879,73.5,,S
667,0,2,"Butler, Mr. Reginald Fenton",male,25,0,0,234686,13,,S
668,0,3,"Rommetvedt, Mr. Knud Paust",male,,0,0,312993,7.775,,S
669,0,3,"Cook, Mr. Jacob",male,43,0,0,A/5 3536,8.05,,S
670,1,1,"Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)",female,,1,0,19996,52,C126,S
671,1,2,"Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)",female,40,1,1,29750,39,,S
672,0,1,"Davidson, Mr. Thornton",male,31,1,0,F.C. 12750,52,B71,S
673,0,2,"Mitchell, Mr. Henry Michael",male,70,0,0,C.A. 24580,10.5,,S
674,1,2,"Wilhelms, Mr. Charles",male,31,0,0,244270,13,,S
675,0,2,"Watson, Mr. Ennis Hastings",male,,0,0,239856,0,,S
676,0,3,"Edvardsson, Mr. Gustaf Hjalmar",male,18,0,0,349912,7.775,,S
677,0,3,"Sawyer, Mr. Frederick Charles",male,24.5,0,0,342826,8.05,,S
678,1,3,"Turja, Miss. Anna Sofia",female,18,0,0,4138,9.8417,,S
679,0,3,"Goodwin, Mrs. Frederick (Augusta Tyler)",female,43,1,6,CA 2144,46.9,,S
680,1,1,"Cardeza, Mr. Thomas Drake Martinez",male,36,0,1,PC 17755,512.3292,B51 B53 B55,C
681,0,3,"Peters, Miss. Katie",female,,0,0,330935,8.1375,,Q
682,1,1,"Hassab, Mr. Hammad",male,27,0,0,PC 17572,76.7292,D49,C
683,0,3,"Olsvigen, Mr. Thor Anderson",male,20,0,0,6563,9.225,,S
684,0,3,"Goodwin, Mr. Charles Edward",male,14,5,2,CA 2144,46.9,,S
685,0,2,"Brown, Mr. Thomas William Solomon",male,60,1,1,29750,39,,S
686,0,2,"Laroche, Mr. Joseph Philippe Lemercier",male,25,1,2,SC/Paris 2123,41.5792,,C
687,0,3,"Panula, Mr. Jaako Arnold",male,14,4,1,3101295,39.6875,,S
688,0,3,"Dakic, Mr. Branko",male,19,0,0,349228,10.1708,,S
689,0,3,"Fischer, Mr. Eberhard Thelander",male,18,0,0,350036,7.7958,,S
690,1,1,"Madill, Miss. Georgette Alexandra",female,15,0,1,24160,211.3375,B5,S
691,1,1,"Dick, Mr. Albert Adrian",male,31,1,0,17474,57,B20,S
692,1,3,"Karun, Miss. Manca",female,4,0,1,349256,13.4167,,C
693,1,3,"Lam, Mr. Ali",male,,0,0,1601,56.4958,,S
694,0,3,"Saad, Mr. Khalil",male,25,0,0,2672,7.225,,C
695,0,1,"Weir, Col. John",male,60,0,0,113800,26.55,,S
696,0,2,"Chapman, Mr. Charles Henry",male,52,0,0,248731,13.5,,S
697,0,3,"Kelly, Mr. James",male,44,0,0,363592,8.05,,S
698,1,3,"Mullens, Miss. Katherine ""Katie""",female,,0,0,35852,7.7333,,Q
699,0,1,"Thayer, Mr. John Borland",male,49,1,1,17421,110.8833,C68,C
700,0,3,"Humblen, Mr. Adolf Mathias Nicolai Olsen",male,42,0,0,348121,7.65,F G63,S
701,1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)",female,18,1,0,PC 17757,227.525,C62 C64,C
702,1,1,"Silverthorne, Mr. Spencer Victor",male,35,0,0,PC 17475,26.2875,E24,S
703,0,3,"Barbara, Miss. Saiide",female,18,0,1,2691,14.4542,,C
704,0,3,"Gallagher, Mr. Martin",male,25,0,0,36864,7.7417,,Q
705,0,3,"Hansen, Mr. Henrik Juul",male,26,1,0,350025,7.8542,,S
706,0,2,"Morley, Mr. Henry Samuel (""Mr Henry Marshall"")",male,39,0,0,250655,26,,S
707,1,2,"Kelly, Mrs. Florence ""Fannie""",female,45,0,0,223596,13.5,,S
708,1,1,"Calderhead, Mr. Edward Pennington",male,42,0,0,PC 17476,26.2875,E24,S
709,1,1,"Cleaver, Miss. Alice",female,22,0,0,113781,151.55,,S
710,1,3,"Moubarek, Master. Halim Gonios (""William George"")",male,,1,1,2661,15.2458,,C
711,1,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",female,24,0,0,PC 17482,49.5042,C90,C
712,0,1,"Klaber, Mr. Herman",male,,0,0,113028,26.55,C124,S
713,1,1,"Taylor, Mr. Elmer Zebley",male,48,1,0,19996,52,C126,S
714,0,3,"Larsson, Mr. August Viktor",male,29,0,0,7545,9.4833,,S
715,0,2,"Greenberg, Mr. Samuel",male,52,0,0,250647,13,,S
716,0,3,"Soholt, Mr. Peter Andreas Lauritz Andersen",male,19,0,0,348124,7.65,F G73,S
717,1,1,"Endres, Miss. Caroline Louise",female,38,0,0,PC 17757,227.525,C45,C
718,1,2,"Troutt, Miss. Edwina Celia ""Winnie""",female,27,0,0,34218,10.5,E101,S
719,0,3,"McEvoy, Mr. Michael",male,,0,0,36568,15.5,,Q
720,0,3,"Johnson, Mr. Malkolm Joackim",male,33,0,0,347062,7.775,,S
721,1,2,"Harper, Miss. Annie Jessie ""Nina""",female,6,0,1,248727,33,,S
722,0,3,"Jensen, Mr. Svend Lauritz",male,17,1,0,350048,7.0542,,S
723,0,2,"Gillespie, Mr. William Henry",male,34,0,0,12233,13,,S
724,0,2,"Hodges, Mr. Henry Price",male,50,0,0,250643,13,,S
725,1,1,"Chambers, Mr. Norman Campbell",male,27,1,0,113806,53.1,E8,S
726,0,3,"Oreskovic, Mr. Luka",male,20,0,0,315094,8.6625,,S
727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30,3,0,31027,21,,S
728,1,3,"Mannion, Miss. Margareth",female,,0,0,36866,7.7375,,Q
729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25,1,0,236853,26,,S
730,0,3,"Ilmakangas, Miss. Pieta Sofia",female,25,1,0,STON/O2. 3101271,7.925,,S
731,1,1,"Allen, Miss. Elisabeth Walton",female,29,0,0,24160,211.3375,B5,S
732,0,3,"Hassan, Mr. Houssein G N",male,11,0,0,2699,18.7875,,C
733,0,2,"Knight, Mr. Robert J",male,,0,0,239855,0,,S
734,0,2,"Berriman, Mr. William John",male,23,0,0,28425,13,,S
735,0,2,"Troupiansky, Mr. Moses Aaron",male,23,0,0,233639,13,,S
736,0,3,"Williams, Mr. Leslie",male,28.5,0,0,54636,16.1,,S
737,0,3,"Ford, Mrs. Edward (Margaret Ann Watson)",female,48,1,3,W./C. 6608,34.375,,S
738,1,1,"Lesurer, Mr. Gustave J",male,35,0,0,PC 17755,512.3292,B101,C
739,0,3,"Ivanoff, Mr. Kanio",male,,0,0,349201,7.8958,,S
740,0,3,"Nankoff, Mr. Minko",male,,0,0,349218,7.8958,,S
741,1,1,"Hawksford, Mr. Walter James",male,,0,0,16988,30,D45,S
742,0,1,"Cavendish, Mr. Tyrell William",male,36,1,0,19877,78.85,C46,S
743,1,1,"Ryerson, Miss. Susan Parker ""Suzette""",female,21,2,2,PC 17608,262.375,B57 B59 B63 B66,C
744,0,3,"McNamee, Mr. Neal",male,24,1,0,376566,16.1,,S
745,1,3,"Stranden, Mr. Juho",male,31,0,0,STON/O 2. 3101288,7.925,,S
746,0,1,"Crosby, Capt. Edward Gifford",male,70,1,1,WE/P 5735,71,B22,S
747,0,3,"Abbott, Mr. Rossmore Edward",male,16,1,1,C.A. 2673,20.25,,S
748,1,2,"Sinkkonen, Miss. Anna",female,30,0,0,250648,13,,S
749,0,1,"Marvin, Mr. Daniel Warner",male,19,1,0,113773,53.1,D30,S
750,0,3,"Connaghton, Mr. Michael",male,31,0,0,335097,7.75,,Q
751,1,2,"Wells, Miss. Joan",female,4,1,1,29103,23,,S
752,1,3,"Moor, Master. Meier",male,6,0,1,392096,12.475,E121,S
753,0,3,"Vande Velde, Mr. Johannes Joseph",male,33,0,0,345780,9.5,,S
754,0,3,"Jonkoff, Mr. Lalio",male,23,0,0,349204,7.8958,,S
755,1,2,"Herman, Mrs. Samuel (Jane Laver)",female,48,1,2,220845,65,,S
756,1,2,"Hamalainen, Master. Viljo",male,0.67,1,1,250649,14.5,,S
757,0,3,"Carlsson, Mr. August Sigfrid",male,28,0,0,350042,7.7958,,S
758,0,2,"Bailey, Mr. Percy Andrew",male,18,0,0,29108,11.5,,S
759,0,3,"Theobald, Mr. Thomas Leonard",male,34,0,0,363294,8.05,,S
760,1,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",female,33,0,0,110152,86.5,B77,S
761,0,3,"Garfirth, Mr. John",male,,0,0,358585,14.5,,S
762,0,3,"Nirva, Mr. Iisakki Antino Aijo",male,41,0,0,SOTON/O2 3101272,7.125,,S
763,1,3,"Barah, Mr. Hanna Assi",male,20,0,0,2663,7.2292,,C
764,1,1,"Carter, Mrs. William Ernest (Lucile Polk)",female,36,1,2,113760,120,B96 B98,S
765,0,3,"Eklund, Mr. Hans Linus",male,16,0,0,347074,7.775,,S
766,1,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51,1,0,13502,77.9583,D11,S
767,0,1,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C
768,0,3,"Mangan, Miss. Mary",female,30.5,0,0,364850,7.75,,Q
769,0,3,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q
770,0,3,"Gronnestad, Mr. Daniel Danielsen",male,32,0,0,8471,8.3625,,S
771,0,3,"Lievens, Mr. Rene Aime",male,24,0,0,345781,9.5,,S
772,0,3,"Jensen, Mr. Niels Peder",male,48,0,0,350047,7.8542,,S
773,0,2,"Mack, Mrs. (Mary)",female,57,0,0,S.O./P.P. 3,10.5,E77,S
774,0,3,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C
775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54,1,3,29105,23,,S
776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18,0,0,347078,7.75,,S
777,0,3,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q
778,1,3,"Emanuel, Miss. Virginia Ethel",female,5,0,0,364516,12.475,,S
779,0,3,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q
780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43,0,1,24160,211.3375,B3,S
781,1,3,"Ayoub, Miss. Banoura",female,13,0,0,2687,7.2292,,C
782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17,1,0,17474,57,B20,S
783,0,1,"Long, Mr. Milton Clyde",male,29,0,0,113501,30,D6,S
784,0,3,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S
785,0,3,"Ali, Mr. William",male,25,0,0,SOTON/O.Q. 3101312,7.05,,S
786,0,3,"Harmer, Mr. Abraham (David Lishin)",male,25,0,0,374887,7.25,,S
787,1,3,"Sjoblom, Miss. Anna Sofia",female,18,0,0,3101265,7.4958,,S
788,0,3,"Rice, Master. George Hugh",male,8,4,1,382652,29.125,,Q
789,1,3,"Dean, Master. Bertram Vere",male,1,1,2,C.A. 2315,20.575,,S
790,0,1,"Guggenheim, Mr. Benjamin",male,46,0,0,PC 17593,79.2,B82 B84,C
791,0,3,"Keane, Mr. Andrew ""Andy""",male,,0,0,12460,7.75,,Q
792,0,2,"Gaskell, Mr. Alfred",male,16,0,0,239865,26,,S
793,0,3,"Sage, Miss. Stella Anna",female,,8,2,CA. 2343,69.55,,S
794,0,1,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C
795,0,3,"Dantcheff, Mr. Ristiu",male,25,0,0,349203,7.8958,,S
796,0,2,"Otter, Mr. Richard",male,39,0,0,28213,13,,S
797,1,1,"Leader, Dr. Alice (Farnham)",female,49,0,0,17465,25.9292,D17,S
798,1,3,"Osman, Mrs. Mara",female,31,0,0,349244,8.6833,,S
799,0,3,"Ibrahim Shawah, Mr. Yousseff",male,30,0,0,2685,7.2292,,C
800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30,1,1,345773,24.15,,S
801,0,2,"Ponesell, Mr. Martin",male,34,0,0,250647,13,,S
802,1,2,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31,1,1,C.A. 31921,26.25,,S
803,1,1,"Carter, Master. William Thornton II",male,11,1,2,113760,120,B96 B98,S
804,1,3,"Thomas, Master. Assad Alexander",male,0.42,0,1,2625,8.5167,,C
805,1,3,"Hedman, Mr. Oskar Arvid",male,27,0,0,347089,6.975,,S
806,0,3,"Johansson, Mr. Karl Johan",male,31,0,0,347063,7.775,,S
807,0,1,"Andrews, Mr. Thomas Jr",male,39,0,0,112050,0,A36,S
808,0,3,"Pettersson, Miss. Ellen Natalia",female,18,0,0,347087,7.775,,S
809,0,2,"Meyer, Mr. August",male,39,0,0,248723,13,,S
810,1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33,1,0,113806,53.1,E8,S
811,0,3,"Alexander, Mr. William",male,26,0,0,3474,7.8875,,S
812,0,3,"Lester, Mr. James",male,39,0,0,A/4 48871,24.15,,S
813,0,2,"Slemen, Mr. Richard James",male,35,0,0,28206,10.5,,S
814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6,4,2,347082,31.275,,S
815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S
816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0,B102,S
817,0,3,"Heininen, Miss. Wendla Maria",female,23,0,0,STON/O2. 3101290,7.925,,S
818,0,2,"Mallet, Mr. Albert",male,31,1,1,S.C./PARIS 2079,37.0042,,C
819,0,3,"Holm, Mr. John Fredrik Alexander",male,43,0,0,C 7075,6.45,,S
820,0,3,"Skoog, Master. Karl Thorsten",male,10,3,2,347088,27.9,,S
821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S
822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S
823,0,1,"Reuchlin, Jonkheer. John George",male,38,0,0,19972,0,,S
824,1,3,"Moor, Mrs. (Beila)",female,27,0,1,392096,12.475,E121,S
825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S
826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q
827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S
828,1,2,"Mallet, Master. Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C
829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q
830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28,
831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C
832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S
833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C
834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S
835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S
836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C
837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S
838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S
839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S
840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C
841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S
842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S
843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C
844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C
845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S
846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S
847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S
848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C
849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S
850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C
851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S
852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S
853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S
855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S
856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S
857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S
863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S
864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S
866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S
867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S
871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S
872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S
874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q
1 PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
2 1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.25 S
3 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0 PC 17599 71.2833 C85 C
4 3 1 3 Heikkinen, Miss. Laina female 26 0 0 STON/O2. 3101282 7.925 S
5 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 113803 53.1 C123 S
6 5 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.05 S
7 6 0 3 Moran, Mr. James male 0 0 330877 8.4583 Q
8 7 0 1 McCarthy, Mr. Timothy J male 54 0 0 17463 51.8625 E46 S
9 8 0 3 Palsson, Master. Gosta Leonard male 2 3 1 349909 21.075 S
10 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27 0 2 347742 11.1333 S
11 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14 1 0 237736 30.0708 C
12 11 1 3 Sandstrom, Miss. Marguerite Rut female 4 1 1 PP 9549 16.7 G6 S
13 12 1 1 Bonnell, Miss. Elizabeth female 58 0 0 113783 26.55 C103 S
14 13 0 3 Saundercock, Mr. William Henry male 20 0 0 A/5. 2151 8.05 S
15 14 0 3 Andersson, Mr. Anders Johan male 39 1 5 347082 31.275 S
16 15 0 3 Vestrom, Miss. Hulda Amanda Adolfina female 14 0 0 350406 7.8542 S
17 16 1 2 Hewlett, Mrs. (Mary D Kingcome) female 55 0 0 248706 16 S
18 17 0 3 Rice, Master. Eugene male 2 4 1 382652 29.125 Q
19 18 1 2 Williams, Mr. Charles Eugene male 0 0 244373 13 S
20 19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele) female 31 1 0 345763 18 S
21 20 1 3 Masselmani, Mrs. Fatima female 0 0 2649 7.225 C
22 21 0 2 Fynney, Mr. Joseph J male 35 0 0 239865 26 S
23 22 1 2 Beesley, Mr. Lawrence male 34 0 0 248698 13 D56 S
24 23 1 3 McGowan, Miss. Anna "Annie" female 15 0 0 330923 8.0292 Q
25 24 1 1 Sloper, Mr. William Thompson male 28 0 0 113788 35.5 A6 S
26 25 0 3 Palsson, Miss. Torborg Danira female 8 3 1 349909 21.075 S
27 26 1 3 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson) female 38 1 5 347077 31.3875 S
28 27 0 3 Emir, Mr. Farred Chehab male 0 0 2631 7.225 C
29 28 0 1 Fortune, Mr. Charles Alexander male 19 3 2 19950 263 C23 C25 C27 S
30 29 1 3 O'Dwyer, Miss. Ellen "Nellie" female 0 0 330959 7.8792 Q
31 30 0 3 Todoroff, Mr. Lalio male 0 0 349216 7.8958 S
32 31 0 1 Uruchurtu, Don. Manuel E male 40 0 0 PC 17601 27.7208 C
33 32 1 1 Spencer, Mrs. William Augustus (Marie Eugenie) female 1 0 PC 17569 146.5208 B78 C
34 33 1 3 Glynn, Miss. Mary Agatha female 0 0 335677 7.75 Q
35 34 0 2 Wheadon, Mr. Edward H male 66 0 0 C.A. 24579 10.5 S
36 35 0 1 Meyer, Mr. Edgar Joseph male 28 1 0 PC 17604 82.1708 C
37 36 0 1 Holverson, Mr. Alexander Oskar male 42 1 0 113789 52 S
38 37 1 3 Mamee, Mr. Hanna male 0 0 2677 7.2292 C
39 38 0 3 Cann, Mr. Ernest Charles male 21 0 0 A./5. 2152 8.05 S
40 39 0 3 Vander Planke, Miss. Augusta Maria female 18 2 0 345764 18 S
41 40 1 3 Nicola-Yarred, Miss. Jamila female 14 1 0 2651 11.2417 C
42 41 0 3 Ahlin, Mrs. Johan (Johanna Persdotter Larsson) female 40 1 0 7546 9.475 S
43 42 0 2 Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott) female 27 1 0 11668 21 S
44 43 0 3 Kraeff, Mr. Theodor male 0 0 349253 7.8958 C
45 44 1 2 Laroche, Miss. Simonne Marie Anne Andree female 3 1 2 SC/Paris 2123 41.5792 C
46 45 1 3 Devaney, Miss. Margaret Delia female 19 0 0 330958 7.8792 Q
47 46 0 3 Rogers, Mr. William John male 0 0 S.C./A.4. 23567 8.05 S
48 47 0 3 Lennon, Mr. Denis male 1 0 370371 15.5 Q
49 48 1 3 O'Driscoll, Miss. Bridget female 0 0 14311 7.75 Q
50 49 0 3 Samaan, Mr. Youssef male 2 0 2662 21.6792 C
51 50 0 3 Arnold-Franchi, Mrs. Josef (Josefine Franchi) female 18 1 0 349237 17.8 S
52 51 0 3 Panula, Master. Juha Niilo male 7 4 1 3101295 39.6875 S
53 52 0 3 Nosworthy, Mr. Richard Cater male 21 0 0 A/4. 39886 7.8 S
54 53 1 1 Harper, Mrs. Henry Sleeper (Myna Haxtun) female 49 1 0 PC 17572 76.7292 D33 C
55 54 1 2 Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson) female 29 1 0 2926 26 S
56 55 0 1 Ostby, Mr. Engelhart Cornelius male 65 0 1 113509 61.9792 B30 C
57 56 1 1 Woolner, Mr. Hugh male 0 0 19947 35.5 C52 S
58 57 1 2 Rugg, Miss. Emily female 21 0 0 C.A. 31026 10.5 S
59 58 0 3 Novel, Mr. Mansouer male 28.5 0 0 2697 7.2292 C
60 59 1 2 West, Miss. Constance Mirium female 5 1 2 C.A. 34651 27.75 S
61 60 0 3 Goodwin, Master. William Frederick male 11 5 2 CA 2144 46.9 S
62 61 0 3 Sirayanian, Mr. Orsen male 22 0 0 2669 7.2292 C
63 62 1 1 Icard, Miss. Amelie female 38 0 0 113572 80 B28
64 63 0 1 Harris, Mr. Henry Birkhardt male 45 1 0 36973 83.475 C83 S
65 64 0 3 Skoog, Master. Harald male 4 3 2 347088 27.9 S
66 65 0 1 Stewart, Mr. Albert A male 0 0 PC 17605 27.7208 C
67 66 1 3 Moubarek, Master. Gerios male 1 1 2661 15.2458 C
68 67 1 2 Nye, Mrs. (Elizabeth Ramell) female 29 0 0 C.A. 29395 10.5 F33 S
69 68 0 3 Crease, Mr. Ernest James male 19 0 0 S.P. 3464 8.1583 S
70 69 1 3 Andersson, Miss. Erna Alexandra female 17 4 2 3101281 7.925 S
71 70 0 3 Kink, Mr. Vincenz male 26 2 0 315151 8.6625 S
72 71 0 2 Jenkin, Mr. Stephen Curnow male 32 0 0 C.A. 33111 10.5 S
73 72 0 3 Goodwin, Miss. Lillian Amy female 16 5 2 CA 2144 46.9 S
74 73 0 2 Hood, Mr. Ambrose Jr male 21 0 0 S.O.C. 14879 73.5 S
75 74 0 3 Chronopoulos, Mr. Apostolos male 26 1 0 2680 14.4542 C
76 75 1 3 Bing, Mr. Lee male 32 0 0 1601 56.4958 S
77 76 0 3 Moen, Mr. Sigurd Hansen male 25 0 0 348123 7.65 F G73 S
78 77 0 3 Staneff, Mr. Ivan male 0 0 349208 7.8958 S
79 78 0 3 Moutal, Mr. Rahamin Haim male 0 0 374746 8.05 S
80 79 1 2 Caldwell, Master. Alden Gates male 0.83 0 2 248738 29 S
81 80 1 3 Dowdell, Miss. Elizabeth female 30 0 0 364516 12.475 S
82 81 0 3 Waelens, Mr. Achille male 22 0 0 345767 9 S
83 82 1 3 Sheerlinck, Mr. Jan Baptist male 29 0 0 345779 9.5 S
84 83 1 3 McDermott, Miss. Brigdet Delia female 0 0 330932 7.7875 Q
85 84 0 1 Carrau, Mr. Francisco M male 28 0 0 113059 47.1 S
86 85 1 2 Ilett, Miss. Bertha female 17 0 0 SO/C 14885 10.5 S
87 86 1 3 Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson) female 33 3 0 3101278 15.85 S
88 87 0 3 Ford, Mr. William Neal male 16 1 3 W./C. 6608 34.375 S
89 88 0 3 Slocovski, Mr. Selman Francis male 0 0 SOTON/OQ 392086 8.05 S
90 89 1 1 Fortune, Miss. Mabel Helen female 23 3 2 19950 263 C23 C25 C27 S
91 90 0 3 Celotti, Mr. Francesco male 24 0 0 343275 8.05 S
92 91 0 3 Christmann, Mr. Emil male 29 0 0 343276 8.05 S
93 92 0 3 Andreasson, Mr. Paul Edvin male 20 0 0 347466 7.8542 S
94 93 0 1 Chaffee, Mr. Herbert Fuller male 46 1 0 W.E.P. 5734 61.175 E31 S
95 94 0 3 Dean, Mr. Bertram Frank male 26 1 2 C.A. 2315 20.575 S
96 95 0 3 Coxon, Mr. Daniel male 59 0 0 364500 7.25 S
97 96 0 3 Shorney, Mr. Charles Joseph male 0 0 374910 8.05 S
98 97 0 1 Goldschmidt, Mr. George B male 71 0 0 PC 17754 34.6542 A5 C
99 98 1 1 Greenfield, Mr. William Bertram male 23 0 1 PC 17759 63.3583 D10 D12 C
100 99 1 2 Doling, Mrs. John T (Ada Julia Bone) female 34 0 1 231919 23 S
101 100 0 2 Kantor, Mr. Sinai male 34 1 0 244367 26 S
102 101 0 3 Petranec, Miss. Matilda female 28 0 0 349245 7.8958 S
103 102 0 3 Petroff, Mr. Pastcho ("Pentcho") male 0 0 349215 7.8958 S
104 103 0 1 White, Mr. Richard Frasar male 21 0 1 35281 77.2875 D26 S
105 104 0 3 Johansson, Mr. Gustaf Joel male 33 0 0 7540 8.6542 S
106 105 0 3 Gustafsson, Mr. Anders Vilhelm male 37 2 0 3101276 7.925 S
107 106 0 3 Mionoff, Mr. Stoytcho male 28 0 0 349207 7.8958 S
108 107 1 3 Salkjelsvik, Miss. Anna Kristine female 21 0 0 343120 7.65 S
109 108 1 3 Moss, Mr. Albert Johan male 0 0 312991 7.775 S
110 109 0 3 Rekic, Mr. Tido male 38 0 0 349249 7.8958 S
111 110 1 3 Moran, Miss. Bertha female 1 0 371110 24.15 Q
112 111 0 1 Porter, Mr. Walter Chamberlain male 47 0 0 110465 52 C110 S
113 112 0 3 Zabour, Miss. Hileni female 14.5 1 0 2665 14.4542 C
114 113 0 3 Barton, Mr. David John male 22 0 0 324669 8.05 S
115 114 0 3 Jussila, Miss. Katriina female 20 1 0 4136 9.825 S
116 115 0 3 Attalah, Miss. Malake female 17 0 0 2627 14.4583 C
117 116 0 3 Pekoniemi, Mr. Edvard male 21 0 0 STON/O 2. 3101294 7.925 S
118 117 0 3 Connors, Mr. Patrick male 70.5 0 0 370369 7.75 Q
119 118 0 2 Turpin, Mr. William John Robert male 29 1 0 11668 21 S
120 119 0 1 Baxter, Mr. Quigg Edmond male 24 0 1 PC 17558 247.5208 B58 B60 C
121 120 0 3 Andersson, Miss. Ellis Anna Maria female 2 4 2 347082 31.275 S
122 121 0 2 Hickman, Mr. Stanley George male 21 2 0 S.O.C. 14879 73.5 S
123 122 0 3 Moore, Mr. Leonard Charles male 0 0 A4. 54510 8.05 S
124 123 0 2 Nasser, Mr. Nicholas male 32.5 1 0 237736 30.0708 C
125 124 1 2 Webber, Miss. Susan female 32.5 0 0 27267 13 E101 S
126 125 0 1 White, Mr. Percival Wayland male 54 0 1 35281 77.2875 D26 S
127 126 1 3 Nicola-Yarred, Master. Elias male 12 1 0 2651 11.2417 C
128 127 0 3 McMahon, Mr. Martin male 0 0 370372 7.75 Q
129 128 1 3 Madsen, Mr. Fridtjof Arne male 24 0 0 C 17369 7.1417 S
130 129 1 3 Peter, Miss. Anna female 1 1 2668 22.3583 F E69 C
131 130 0 3 Ekstrom, Mr. Johan male 45 0 0 347061 6.975 S
132 131 0 3 Drazenoic, Mr. Jozef male 33 0 0 349241 7.8958 C
133 132 0 3 Coelho, Mr. Domingos Fernandeo male 20 0 0 SOTON/O.Q. 3101307 7.05 S
134 133 0 3 Robins, Mrs. Alexander A (Grace Charity Laury) female 47 1 0 A/5. 3337 14.5 S
135 134 1 2 Weisz, Mrs. Leopold (Mathilde Francoise Pede) female 29 1 0 228414 26 S
136 135 0 2 Sobey, Mr. Samuel James Hayden male 25 0 0 C.A. 29178 13 S
137 136 0 2 Richard, Mr. Emile male 23 0 0 SC/PARIS 2133 15.0458 C
138 137 1 1 Newsom, Miss. Helen Monypeny female 19 0 2 11752 26.2833 D47 S
139 138 0 1 Futrelle, Mr. Jacques Heath male 37 1 0 113803 53.1 C123 S
140 139 0 3 Osen, Mr. Olaf Elon male 16 0 0 7534 9.2167 S
141 140 0 1 Giglio, Mr. Victor male 24 0 0 PC 17593 79.2 B86 C
142 141 0 3 Boulos, Mrs. Joseph (Sultana) female 0 2 2678 15.2458 C
143 142 1 3 Nysten, Miss. Anna Sofia female 22 0 0 347081 7.75 S
144 143 1 3 Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck) female 24 1 0 STON/O2. 3101279 15.85 S
145 144 0 3 Burke, Mr. Jeremiah male 19 0 0 365222 6.75 Q
146 145 0 2 Andrew, Mr. Edgardo Samuel male 18 0 0 231945 11.5 S
147 146 0 2 Nicholls, Mr. Joseph Charles male 19 1 1 C.A. 33112 36.75 S
148 147 1 3 Andersson, Mr. August Edvard ("Wennerstrom") male 27 0 0 350043 7.7958 S
149 148 0 3 Ford, Miss. Robina Maggie "Ruby" female 9 2 2 W./C. 6608 34.375 S
150 149 0 2 Navratil, Mr. Michel ("Louis M Hoffman") male 36.5 0 2 230080 26 F2 S
151 150 0 2 Byles, Rev. Thomas Roussel Davids male 42 0 0 244310 13 S
152 151 0 2 Bateman, Rev. Robert James male 51 0 0 S.O.P. 1166 12.525 S
153 152 1 1 Pears, Mrs. Thomas (Edith Wearne) female 22 1 0 113776 66.6 C2 S
154 153 0 3 Meo, Mr. Alfonzo male 55.5 0 0 A.5. 11206 8.05 S
155 154 0 3 van Billiard, Mr. Austin Blyler male 40.5 0 2 A/5. 851 14.5 S
156 155 0 3 Olsen, Mr. Ole Martin male 0 0 Fa 265302 7.3125 S
157 156 0 1 Williams, Mr. Charles Duane male 51 0 1 PC 17597 61.3792 C
158 157 1 3 Gilnagh, Miss. Katherine "Katie" female 16 0 0 35851 7.7333 Q
159 158 0 3 Corn, Mr. Harry male 30 0 0 SOTON/OQ 392090 8.05 S
160 159 0 3 Smiljanic, Mr. Mile male 0 0 315037 8.6625 S
161 160 0 3 Sage, Master. Thomas Henry male 8 2 CA. 2343 69.55 S
162 161 0 3 Cribb, Mr. John Hatfield male 44 0 1 371362 16.1 S
163 162 1 2 Watt, Mrs. James (Elizabeth "Bessie" Inglis Milne) female 40 0 0 C.A. 33595 15.75 S
164 163 0 3 Bengtsson, Mr. John Viktor male 26 0 0 347068 7.775 S
165 164 0 3 Calic, Mr. Jovo male 17 0 0 315093 8.6625 S
166 165 0 3 Panula, Master. Eino Viljami male 1 4 1 3101295 39.6875 S
167 166 1 3 Goldsmith, Master. Frank John William "Frankie" male 9 0 2 363291 20.525 S
168 167 1 1 Chibnall, Mrs. (Edith Martha Bowerman) female 0 1 113505 55 E33 S
169 168 0 3 Skoog, Mrs. William (Anna Bernhardina Karlsson) female 45 1 4 347088 27.9 S
170 169 0 1 Baumann, Mr. John D male 0 0 PC 17318 25.925 S
171 170 0 3 Ling, Mr. Lee male 28 0 0 1601 56.4958 S
172 171 0 1 Van der hoef, Mr. Wyckoff male 61 0 0 111240 33.5 B19 S
173 172 0 3 Rice, Master. Arthur male 4 4 1 382652 29.125 Q
174 173 1 3 Johnson, Miss. Eleanor Ileen female 1 1 1 347742 11.1333 S
175 174 0 3 Sivola, Mr. Antti Wilhelm male 21 0 0 STON/O 2. 3101280 7.925 S
176 175 0 1 Smith, Mr. James Clinch male 56 0 0 17764 30.6958 A7 C
177 176 0 3 Klasen, Mr. Klas Albin male 18 1 1 350404 7.8542 S
178 177 0 3 Lefebre, Master. Henry Forbes male 3 1 4133 25.4667 S
179 178 0 1 Isham, Miss. Ann Elizabeth female 50 0 0 PC 17595 28.7125 C49 C
180 179 0 2 Hale, Mr. Reginald male 30 0 0 250653 13 S
181 180 0 3 Leonard, Mr. Lionel male 36 0 0 LINE 0 S
182 181 0 3 Sage, Miss. Constance Gladys female 8 2 CA. 2343 69.55 S
183 182 0 2 Pernot, Mr. Rene male 0 0 SC/PARIS 2131 15.05 C
184 183 0 3 Asplund, Master. Clarence Gustaf Hugo male 9 4 2 347077 31.3875 S
185 184 1 2 Becker, Master. Richard F male 1 2 1 230136 39 F4 S
186 185 1 3 Kink-Heilmann, Miss. Luise Gretchen female 4 0 2 315153 22.025 S
187 186 0 1 Rood, Mr. Hugh Roscoe male 0 0 113767 50 A32 S
188 187 1 3 O'Brien, Mrs. Thomas (Johanna "Hannah" Godfrey) female 1 0 370365 15.5 Q
189 188 1 1 Romaine, Mr. Charles Hallace ("Mr C Rolmane") male 45 0 0 111428 26.55 S
190 189 0 3 Bourke, Mr. John male 40 1 1 364849 15.5 Q
191 190 0 3 Turcin, Mr. Stjepan male 36 0 0 349247 7.8958 S
192 191 1 2 Pinsky, Mrs. (Rosa) female 32 0 0 234604 13 S
193 192 0 2 Carbines, Mr. William male 19 0 0 28424 13 S
194 193 1 3 Andersen-Jensen, Miss. Carla Christine Nielsine female 19 1 0 350046 7.8542 S
195 194 1 2 Navratil, Master. Michel M male 3 1 1 230080 26 F2 S
196 195 1 1 Brown, Mrs. James Joseph (Margaret Tobin) female 44 0 0 PC 17610 27.7208 B4 C
197 196 1 1 Lurette, Miss. Elise female 58 0 0 PC 17569 146.5208 B80 C
198 197 0 3 Mernagh, Mr. Robert male 0 0 368703 7.75 Q
199 198 0 3 Olsen, Mr. Karl Siegwart Andreas male 42 0 1 4579 8.4042 S
200 199 1 3 Madigan, Miss. Margaret "Maggie" female 0 0 370370 7.75 Q
201 200 0 2 Yrois, Miss. Henriette ("Mrs Harbeck") female 24 0 0 248747 13 S
202 201 0 3 Vande Walle, Mr. Nestor Cyriel male 28 0 0 345770 9.5 S
203 202 0 3 Sage, Mr. Frederick male 8 2 CA. 2343 69.55 S
204 203 0 3 Johanson, Mr. Jakob Alfred male 34 0 0 3101264 6.4958 S
205 204 0 3 Youseff, Mr. Gerious male 45.5 0 0 2628 7.225 C
206 205 1 3 Cohen, Mr. Gurshon "Gus" male 18 0 0 A/5 3540 8.05 S
207 206 0 3 Strom, Miss. Telma Matilda female 2 0 1 347054 10.4625 G6 S
208 207 0 3 Backstrom, Mr. Karl Alfred male 32 1 0 3101278 15.85 S
209 208 1 3 Albimona, Mr. Nassef Cassem male 26 0 0 2699 18.7875 C
210 209 1 3 Carr, Miss. Helen "Ellen" female 16 0 0 367231 7.75 Q
211 210 1 1 Blank, Mr. Henry male 40 0 0 112277 31 A31 C
212 211 0 3 Ali, Mr. Ahmed male 24 0 0 SOTON/O.Q. 3101311 7.05 S
213 212 1 2 Cameron, Miss. Clear Annie female 35 0 0 F.C.C. 13528 21 S
214 213 0 3 Perkin, Mr. John Henry male 22 0 0 A/5 21174 7.25 S
215 214 0 2 Givard, Mr. Hans Kristensen male 30 0 0 250646 13 S
216 215 0 3 Kiernan, Mr. Philip male 1 0 367229 7.75 Q
217 216 1 1 Newell, Miss. Madeleine female 31 1 0 35273 113.275 D36 C
218 217 1 3 Honkanen, Miss. Eliina female 27 0 0 STON/O2. 3101283 7.925 S
219 218 0 2 Jacobsohn, Mr. Sidney Samuel male 42 1 0 243847 27 S
220 219 1 1 Bazzani, Miss. Albina female 32 0 0 11813 76.2917 D15 C
221 220 0 2 Harris, Mr. Walter male 30 0 0 W/C 14208 10.5 S
222 221 1 3 Sunderland, Mr. Victor Francis male 16 0 0 SOTON/OQ 392089 8.05 S
223 222 0 2 Bracken, Mr. James H male 27 0 0 220367 13 S
224 223 0 3 Green, Mr. George Henry male 51 0 0 21440 8.05 S
225 224 0 3 Nenkoff, Mr. Christo male 0 0 349234 7.8958 S
226 225 1 1 Hoyt, Mr. Frederick Maxfield male 38 1 0 19943 90 C93 S
227 226 0 3 Berglund, Mr. Karl Ivar Sven male 22 0 0 PP 4348 9.35 S
228 227 1 2 Mellors, Mr. William John male 19 0 0 SW/PP 751 10.5 S
229 228 0 3 Lovell, Mr. John Hall ("Henry") male 20.5 0 0 A/5 21173 7.25 S
230 229 0 2 Fahlstrom, Mr. Arne Jonas male 18 0 0 236171 13 S
231 230 0 3 Lefebre, Miss. Mathilde female 3 1 4133 25.4667 S
232 231 1 1 Harris, Mrs. Henry Birkhardt (Irene Wallach) female 35 1 0 36973 83.475 C83 S
233 232 0 3 Larsson, Mr. Bengt Edvin male 29 0 0 347067 7.775 S
234 233 0 2 Sjostedt, Mr. Ernst Adolf male 59 0 0 237442 13.5 S
235 234 1 3 Asplund, Miss. Lillian Gertrud female 5 4 2 347077 31.3875 S
236 235 0 2 Leyson, Mr. Robert William Norman male 24 0 0 C.A. 29566 10.5 S
237 236 0 3 Harknett, Miss. Alice Phoebe female 0 0 W./C. 6609 7.55 S
238 237 0 2 Hold, Mr. Stephen male 44 1 0 26707 26 S
239 238 1 2 Collyer, Miss. Marjorie "Lottie" female 8 0 2 C.A. 31921 26.25 S
240 239 0 2 Pengelly, Mr. Frederick William male 19 0 0 28665 10.5 S
241 240 0 2 Hunt, Mr. George Henry male 33 0 0 SCO/W 1585 12.275 S
242 241 0 3 Zabour, Miss. Thamine female 1 0 2665 14.4542 C
243 242 1 3 Murphy, Miss. Katherine "Kate" female 1 0 367230 15.5 Q
244 243 0 2 Coleridge, Mr. Reginald Charles male 29 0 0 W./C. 14263 10.5 S
245 244 0 3 Maenpaa, Mr. Matti Alexanteri male 22 0 0 STON/O 2. 3101275 7.125 S
246 245 0 3 Attalah, Mr. Sleiman male 30 0 0 2694 7.225 C
247 246 0 1 Minahan, Dr. William Edward male 44 2 0 19928 90 C78 Q
248 247 0 3 Lindahl, Miss. Agda Thorilda Viktoria female 25 0 0 347071 7.775 S
249 248 1 2 Hamalainen, Mrs. William (Anna) female 24 0 2 250649 14.5 S
250 249 1 1 Beckwith, Mr. Richard Leonard male 37 1 1 11751 52.5542 D35 S
251 250 0 2 Carter, Rev. Ernest Courtenay male 54 1 0 244252 26 S
252 251 0 3 Reed, Mr. James George male 0 0 362316 7.25 S
253 252 0 3 Strom, Mrs. Wilhelm (Elna Matilda Persson) female 29 1 1 347054 10.4625 G6 S
254 253 0 1 Stead, Mr. William Thomas male 62 0 0 113514 26.55 C87 S
255 254 0 3 Lobb, Mr. William Arthur male 30 1 0 A/5. 3336 16.1 S
256 255 0 3 Rosblom, Mrs. Viktor (Helena Wilhelmina) female 41 0 2 370129 20.2125 S
257 256 1 3 Touma, Mrs. Darwis (Hanne Youssef Razi) female 29 0 2 2650 15.2458 C
258 257 1 1 Thorne, Mrs. Gertrude Maybelle female 0 0 PC 17585 79.2 C
259 258 1 1 Cherry, Miss. Gladys female 30 0 0 110152 86.5 B77 S
260 259 1 1 Ward, Miss. Anna female 35 0 0 PC 17755 512.3292 C
261 260 1 2 Parrish, Mrs. (Lutie Davis) female 50 0 1 230433 26 S
262 261 0 3 Smith, Mr. Thomas male 0 0 384461 7.75 Q
263 262 1 3 Asplund, Master. Edvin Rojj Felix male 3 4 2 347077 31.3875 S
264 263 0 1 Taussig, Mr. Emil male 52 1 1 110413 79.65 E67 S
265 264 0 1 Harrison, Mr. William male 40 0 0 112059 0 B94 S
266 265 0 3 Henry, Miss. Delia female 0 0 382649 7.75 Q
267 266 0 2 Reeves, Mr. David male 36 0 0 C.A. 17248 10.5 S
268 267 0 3 Panula, Mr. Ernesti Arvid male 16 4 1 3101295 39.6875 S
269 268 1 3 Persson, Mr. Ernst Ulrik male 25 1 0 347083 7.775 S
270 269 1 1 Graham, Mrs. William Thompson (Edith Junkins) female 58 0 1 PC 17582 153.4625 C125 S
271 270 1 1 Bissette, Miss. Amelia female 35 0 0 PC 17760 135.6333 C99 S
272 271 0 1 Cairns, Mr. Alexander male 0 0 113798 31 S
273 272 1 3 Tornquist, Mr. William Henry male 25 0 0 LINE 0 S
274 273 1 2 Mellinger, Mrs. (Elizabeth Anne Maidment) female 41 0 1 250644 19.5 S
275 274 0 1 Natsch, Mr. Charles H male 37 0 1 PC 17596 29.7 C118 C
276 275 1 3 Healy, Miss. Hanora "Nora" female 0 0 370375 7.75 Q
277 276 1 1 Andrews, Miss. Kornelia Theodosia female 63 1 0 13502 77.9583 D7 S
278 277 0 3 Lindblom, Miss. Augusta Charlotta female 45 0 0 347073 7.75 S
279 278 0 2 Parkes, Mr. Francis "Frank" male 0 0 239853 0 S
280 279 0 3 Rice, Master. Eric male 7 4 1 382652 29.125 Q
281 280 1 3 Abbott, Mrs. Stanton (Rosa Hunt) female 35 1 1 C.A. 2673 20.25 S
282 281 0 3 Duane, Mr. Frank male 65 0 0 336439 7.75 Q
283 282 0 3 Olsson, Mr. Nils Johan Goransson male 28 0 0 347464 7.8542 S
284 283 0 3 de Pelsmaeker, Mr. Alfons male 16 0 0 345778 9.5 S
285 284 1 3 Dorking, Mr. Edward Arthur male 19 0 0 A/5. 10482 8.05 S
286 285 0 1 Smith, Mr. Richard William male 0 0 113056 26 A19 S
287 286 0 3 Stankovic, Mr. Ivan male 33 0 0 349239 8.6625 C
288 287 1 3 de Mulder, Mr. Theodore male 30 0 0 345774 9.5 S
289 288 0 3 Naidenoff, Mr. Penko male 22 0 0 349206 7.8958 S
290 289 1 2 Hosono, Mr. Masabumi male 42 0 0 237798 13 S
291 290 1 3 Connolly, Miss. Kate female 22 0 0 370373 7.75 Q
292 291 1 1 Barber, Miss. Ellen "Nellie" female 26 0 0 19877 78.85 S
293 292 1 1 Bishop, Mrs. Dickinson H (Helen Walton) female 19 1 0 11967 91.0792 B49 C
294 293 0 2 Levy, Mr. Rene Jacques male 36 0 0 SC/Paris 2163 12.875 D C
295 294 0 3 Haas, Miss. Aloisia female 24 0 0 349236 8.85 S
296 295 0 3 Mineff, Mr. Ivan male 24 0 0 349233 7.8958 S
297 296 0 1 Lewy, Mr. Ervin G male 0 0 PC 17612 27.7208 C
298 297 0 3 Hanna, Mr. Mansour male 23.5 0 0 2693 7.2292 C
299 298 0 1 Allison, Miss. Helen Loraine female 2 1 2 113781 151.55 C22 C26 S
300 299 1 1 Saalfeld, Mr. Adolphe male 0 0 19988 30.5 C106 S
301 300 1 1 Baxter, Mrs. James (Helene DeLaudeniere Chaput) female 50 0 1 PC 17558 247.5208 B58 B60 C
302 301 1 3 Kelly, Miss. Anna Katherine "Annie Kate" female 0 0 9234 7.75 Q
303 302 1 3 McCoy, Mr. Bernard male 2 0 367226 23.25 Q
304 303 0 3 Johnson, Mr. William Cahoone Jr male 19 0 0 LINE 0 S
305 304 1 2 Keane, Miss. Nora A female 0 0 226593 12.35 E101 Q
306 305 0 3 Williams, Mr. Howard Hugh "Harry" male 0 0 A/5 2466 8.05 S
307 306 1 1 Allison, Master. Hudson Trevor male 0.92 1 2 113781 151.55 C22 C26 S
308 307 1 1 Fleming, Miss. Margaret female 0 0 17421 110.8833 C
309 308 1 1 Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo) female 17 1 0 PC 17758 108.9 C65 C
310 309 0 2 Abelson, Mr. Samuel male 30 1 0 P/PP 3381 24 C
311 310 1 1 Francatelli, Miss. Laura Mabel female 30 0 0 PC 17485 56.9292 E36 C
312 311 1 1 Hays, Miss. Margaret Bechstein female 24 0 0 11767 83.1583 C54 C
313 312 1 1 Ryerson, Miss. Emily Borie female 18 2 2 PC 17608 262.375 B57 B59 B63 B66 C
314 313 0 2 Lahtinen, Mrs. William (Anna Sylfven) female 26 1 1 250651 26 S
315 314 0 3 Hendekovic, Mr. Ignjac male 28 0 0 349243 7.8958 S
316 315 0 2 Hart, Mr. Benjamin male 43 1 1 F.C.C. 13529 26.25 S
317 316 1 3 Nilsson, Miss. Helmina Josefina female 26 0 0 347470 7.8542 S
318 317 1 2 Kantor, Mrs. Sinai (Miriam Sternin) female 24 1 0 244367 26 S
319 318 0 2 Moraweck, Dr. Ernest male 54 0 0 29011 14 S
320 319 1 1 Wick, Miss. Mary Natalie female 31 0 2 36928 164.8667 C7 S
321 320 1 1 Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone) female 40 1 1 16966 134.5 E34 C
322 321 0 3 Dennis, Mr. Samuel male 22 0 0 A/5 21172 7.25 S
323 322 0 3 Danoff, Mr. Yoto male 27 0 0 349219 7.8958 S
324 323 1 2 Slayter, Miss. Hilda Mary female 30 0 0 234818 12.35 Q
325 324 1 2 Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh) female 22 1 1 248738 29 S
326 325 0 3 Sage, Mr. George John Jr male 8 2 CA. 2343 69.55 S
327 326 1 1 Young, Miss. Marie Grice female 36 0 0 PC 17760 135.6333 C32 C
328 327 0 3 Nysveen, Mr. Johan Hansen male 61 0 0 345364 6.2375 S
329 328 1 2 Ball, Mrs. (Ada E Hall) female 36 0 0 28551 13 D S
330 329 1 3 Goldsmith, Mrs. Frank John (Emily Alice Brown) female 31 1 1 363291 20.525 S
331 330 1 1 Hippach, Miss. Jean Gertrude female 16 0 1 111361 57.9792 B18 C
332 331 1 3 McCoy, Miss. Agnes female 2 0 367226 23.25 Q
333 332 0 1 Partner, Mr. Austen male 45.5 0 0 113043 28.5 C124 S
334 333 0 1 Graham, Mr. George Edward male 38 0 1 PC 17582 153.4625 C91 S
335 334 0 3 Vander Planke, Mr. Leo Edmondus male 16 2 0 345764 18 S
336 335 1 1 Frauenthal, Mrs. Henry William (Clara Heinsheimer) female 1 0 PC 17611 133.65 S
337 336 0 3 Denkoff, Mr. Mitto male 0 0 349225 7.8958 S
338 337 0 1 Pears, Mr. Thomas Clinton male 29 1 0 113776 66.6 C2 S
339 338 1 1 Burns, Miss. Elizabeth Margaret female 41 0 0 16966 134.5 E40 C
340 339 1 3 Dahl, Mr. Karl Edwart male 45 0 0 7598 8.05 S
341 340 0 1 Blackwell, Mr. Stephen Weart male 45 0 0 113784 35.5 T S
342 341 1 2 Navratil, Master. Edmond Roger male 2 1 1 230080 26 F2 S
343 342 1 1 Fortune, Miss. Alice Elizabeth female 24 3 2 19950 263 C23 C25 C27 S
344 343 0 2 Collander, Mr. Erik Gustaf male 28 0 0 248740 13 S
345 344 0 2 Sedgwick, Mr. Charles Frederick Waddington male 25 0 0 244361 13 S
346 345 0 2 Fox, Mr. Stanley Hubert male 36 0 0 229236 13 S
347 346 1 2 Brown, Miss. Amelia "Mildred" female 24 0 0 248733 13 F33 S
348 347 1 2 Smith, Miss. Marion Elsie female 40 0 0 31418 13 S
349 348 1 3 Davison, Mrs. Thomas Henry (Mary E Finck) female 1 0 386525 16.1 S
350 349 1 3 Coutts, Master. William Loch "William" male 3 1 1 C.A. 37671 15.9 S
351 350 0 3 Dimic, Mr. Jovan male 42 0 0 315088 8.6625 S
352 351 0 3 Odahl, Mr. Nils Martin male 23 0 0 7267 9.225 S
353 352 0 1 Williams-Lambert, Mr. Fletcher Fellows male 0 0 113510 35 C128 S
354 353 0 3 Elias, Mr. Tannous male 15 1 1 2695 7.2292 C
355 354 0 3 Arnold-Franchi, Mr. Josef male 25 1 0 349237 17.8 S
356 355 0 3 Yousif, Mr. Wazli male 0 0 2647 7.225 C
357 356 0 3 Vanden Steen, Mr. Leo Peter male 28 0 0 345783 9.5 S
358 357 1 1 Bowerman, Miss. Elsie Edith female 22 0 1 113505 55 E33 S
359 358 0 2 Funk, Miss. Annie Clemmer female 38 0 0 237671 13 S
360 359 1 3 McGovern, Miss. Mary female 0 0 330931 7.8792 Q
361 360 1 3 Mockler, Miss. Helen Mary "Ellie" female 0 0 330980 7.8792 Q
362 361 0 3 Skoog, Mr. Wilhelm male 40 1 4 347088 27.9 S
363 362 0 2 del Carlo, Mr. Sebastiano male 29 1 0 SC/PARIS 2167 27.7208 C
364 363 0 3 Barbara, Mrs. (Catherine David) female 45 0 1 2691 14.4542 C
365 364 0 3 Asim, Mr. Adola male 35 0 0 SOTON/O.Q. 3101310 7.05 S
366 365 0 3 O'Brien, Mr. Thomas male 1 0 370365 15.5 Q
367 366 0 3 Adahl, Mr. Mauritz Nils Martin male 30 0 0 C 7076 7.25 S
368 367 1 1 Warren, Mrs. Frank Manley (Anna Sophia Atkinson) female 60 1 0 110813 75.25 D37 C
369 368 1 3 Moussa, Mrs. (Mantoura Boulos) female 0 0 2626 7.2292 C
370 369 1 3 Jermyn, Miss. Annie female 0 0 14313 7.75 Q
371 370 1 1 Aubart, Mme. Leontine Pauline female 24 0 0 PC 17477 69.3 B35 C
372 371 1 1 Harder, Mr. George Achilles male 25 1 0 11765 55.4417 E50 C
373 372 0 3 Wiklund, Mr. Jakob Alfred male 18 1 0 3101267 6.4958 S
374 373 0 3 Beavan, Mr. William Thomas male 19 0 0 323951 8.05 S
375 374 0 1 Ringhini, Mr. Sante male 22 0 0 PC 17760 135.6333 C
376 375 0 3 Palsson, Miss. Stina Viola female 3 3 1 349909 21.075 S
377 376 1 1 Meyer, Mrs. Edgar Joseph (Leila Saks) female 1 0 PC 17604 82.1708 C
378 377 1 3 Landergren, Miss. Aurora Adelia female 22 0 0 C 7077 7.25 S
379 378 0 1 Widener, Mr. Harry Elkins male 27 0 2 113503 211.5 C82 C
380 379 0 3 Betros, Mr. Tannous male 20 0 0 2648 4.0125 C
381 380 0 3 Gustafsson, Mr. Karl Gideon male 19 0 0 347069 7.775 S
382 381 1 1 Bidois, Miss. Rosalie female 42 0 0 PC 17757 227.525 C
383 382 1 3 Nakid, Miss. Maria ("Mary") female 1 0 2 2653 15.7417 C
384 383 0 3 Tikkanen, Mr. Juho male 32 0 0 STON/O 2. 3101293 7.925 S
385 384 1 1 Holverson, Mrs. Alexander Oskar (Mary Aline Towner) female 35 1 0 113789 52 S
386 385 0 3 Plotcharsky, Mr. Vasil male 0 0 349227 7.8958 S
387 386 0 2 Davies, Mr. Charles Henry male 18 0 0 S.O.C. 14879 73.5 S
388 387 0 3 Goodwin, Master. Sidney Leonard male 1 5 2 CA 2144 46.9 S
389 388 1 2 Buss, Miss. Kate female 36 0 0 27849 13 S
390 389 0 3 Sadlier, Mr. Matthew male 0 0 367655 7.7292 Q
391 390 1 2 Lehmann, Miss. Bertha female 17 0 0 SC 1748 12 C
392 391 1 1 Carter, Mr. William Ernest male 36 1 2 113760 120 B96 B98 S
393 392 1 3 Jansson, Mr. Carl Olof male 21 0 0 350034 7.7958 S
394 393 0 3 Gustafsson, Mr. Johan Birger male 28 2 0 3101277 7.925 S
395 394 1 1 Newell, Miss. Marjorie female 23 1 0 35273 113.275 D36 C
396 395 1 3 Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson) female 24 0 2 PP 9549 16.7 G6 S
397 396 0 3 Johansson, Mr. Erik male 22 0 0 350052 7.7958 S
398 397 0 3 Olsson, Miss. Elina female 31 0 0 350407 7.8542 S
399 398 0 2 McKane, Mr. Peter David male 46 0 0 28403 26 S
400 399 0 2 Pain, Dr. Alfred male 23 0 0 244278 10.5 S
401 400 1 2 Trout, Mrs. William H (Jessie L) female 28 0 0 240929 12.65 S
402 401 1 3 Niskanen, Mr. Juha male 39 0 0 STON/O 2. 3101289 7.925 S
403 402 0 3 Adams, Mr. John male 26 0 0 341826 8.05 S
404 403 0 3 Jussila, Miss. Mari Aina female 21 1 0 4137 9.825 S
405 404 0 3 Hakkarainen, Mr. Pekka Pietari male 28 1 0 STON/O2. 3101279 15.85 S
406 405 0 3 Oreskovic, Miss. Marija female 20 0 0 315096 8.6625 S
407 406 0 2 Gale, Mr. Shadrach male 34 1 0 28664 21 S
408 407 0 3 Widegren, Mr. Carl/Charles Peter male 51 0 0 347064 7.75 S
409 408 1 2 Richards, Master. William Rowe male 3 1 1 29106 18.75 S
410 409 0 3 Birkeland, Mr. Hans Martin Monsen male 21 0 0 312992 7.775 S
411 410 0 3 Lefebre, Miss. Ida female 3 1 4133 25.4667 S
412 411 0 3 Sdycoff, Mr. Todor male 0 0 349222 7.8958 S
413 412 0 3 Hart, Mr. Henry male 0 0 394140 6.8583 Q
414 413 1 1 Minahan, Miss. Daisy E female 33 1 0 19928 90 C78 Q
415 414 0 2 Cunningham, Mr. Alfred Fleming male 0 0 239853 0 S
416 415 1 3 Sundman, Mr. Johan Julian male 44 0 0 STON/O 2. 3101269 7.925 S
417 416 0 3 Meek, Mrs. Thomas (Annie Louise Rowley) female 0 0 343095 8.05 S
418 417 1 2 Drew, Mrs. James Vivian (Lulu Thorne Christian) female 34 1 1 28220 32.5 S
419 418 1 2 Silven, Miss. Lyyli Karoliina female 18 0 2 250652 13 S
420 419 0 2 Matthews, Mr. William John male 30 0 0 28228 13 S
421 420 0 3 Van Impe, Miss. Catharina female 10 0 2 345773 24.15 S
422 421 0 3 Gheorgheff, Mr. Stanio male 0 0 349254 7.8958 C
423 422 0 3 Charters, Mr. David male 21 0 0 A/5. 13032 7.7333 Q
424 423 0 3 Zimmerman, Mr. Leo male 29 0 0 315082 7.875 S
425 424 0 3 Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren) female 28 1 1 347080 14.4 S
426 425 0 3 Rosblom, Mr. Viktor Richard male 18 1 1 370129 20.2125 S
427 426 0 3 Wiseman, Mr. Phillippe male 0 0 A/4. 34244 7.25 S
428 427 1 2 Clarke, Mrs. Charles V (Ada Maria Winfield) female 28 1 0 2003 26 S
429 428 1 2 Phillips, Miss. Kate Florence ("Mrs Kate Louise Phillips Marshall") female 19 0 0 250655 26 S
430 429 0 3 Flynn, Mr. James male 0 0 364851 7.75 Q
431 430 1 3 Pickard, Mr. Berk (Berk Trembisky) male 32 0 0 SOTON/O.Q. 392078 8.05 E10 S
432 431 1 1 Bjornstrom-Steffansson, Mr. Mauritz Hakan male 28 0 0 110564 26.55 C52 S
433 432 1 3 Thorneycroft, Mrs. Percival (Florence Kate White) female 1 0 376564 16.1 S
434 433 1 2 Louch, Mrs. Charles Alexander (Alice Adelaide Slow) female 42 1 0 SC/AH 3085 26 S
435 434 0 3 Kallio, Mr. Nikolai Erland male 17 0 0 STON/O 2. 3101274 7.125 S
436 435 0 1 Silvey, Mr. William Baird male 50 1 0 13507 55.9 E44 S
437 436 1 1 Carter, Miss. Lucile Polk female 14 1 2 113760 120 B96 B98 S
438 437 0 3 Ford, Miss. Doolina Margaret "Daisy" female 21 2 2 W./C. 6608 34.375 S
439 438 1 2 Richards, Mrs. Sidney (Emily Hocking) female 24 2 3 29106 18.75 S
440 439 0 1 Fortune, Mr. Mark male 64 1 4 19950 263 C23 C25 C27 S
441 440 0 2 Kvillner, Mr. Johan Henrik Johannesson male 31 0 0 C.A. 18723 10.5 S
442 441 1 2 Hart, Mrs. Benjamin (Esther Ada Bloomfield) female 45 1 1 F.C.C. 13529 26.25 S
443 442 0 3 Hampe, Mr. Leon male 20 0 0 345769 9.5 S
444 443 0 3 Petterson, Mr. Johan Emil male 25 1 0 347076 7.775 S
445 444 1 2 Reynaldo, Ms. Encarnacion female 28 0 0 230434 13 S
446 445 1 3 Johannesen-Bratthammer, Mr. Bernt male 0 0 65306 8.1125 S
447 446 1 1 Dodge, Master. Washington male 4 0 2 33638 81.8583 A34 S
448 447 1 2 Mellinger, Miss. Madeleine Violet female 13 0 1 250644 19.5 S
449 448 1 1 Seward, Mr. Frederic Kimber male 34 0 0 113794 26.55 S
450 449 1 3 Baclini, Miss. Marie Catherine female 5 2 1 2666 19.2583 C
451 450 1 1 Peuchen, Major. Arthur Godfrey male 52 0 0 113786 30.5 C104 S
452 451 0 2 West, Mr. Edwy Arthur male 36 1 2 C.A. 34651 27.75 S
453 452 0 3 Hagland, Mr. Ingvald Olai Olsen male 1 0 65303 19.9667 S
454 453 0 1 Foreman, Mr. Benjamin Laventall male 30 0 0 113051 27.75 C111 C
455 454 1 1 Goldenberg, Mr. Samuel L male 49 1 0 17453 89.1042 C92 C
456 455 0 3 Peduzzi, Mr. Joseph male 0 0 A/5 2817 8.05 S
457 456 1 3 Jalsevac, Mr. Ivan male 29 0 0 349240 7.8958 C
458 457 0 1 Millet, Mr. Francis Davis male 65 0 0 13509 26.55 E38 S
459 458 1 1 Kenyon, Mrs. Frederick R (Marion) female 1 0 17464 51.8625 D21 S
460 459 1 2 Toomey, Miss. Ellen female 50 0 0 F.C.C. 13531 10.5 S
461 460 0 3 O'Connor, Mr. Maurice male 0 0 371060 7.75 Q
462 461 1 1 Anderson, Mr. Harry male 48 0 0 19952 26.55 E12 S
463 462 0 3 Morley, Mr. William male 34 0 0 364506 8.05 S
464 463 0 1 Gee, Mr. Arthur H male 47 0 0 111320 38.5 E63 S
465 464 0 2 Milling, Mr. Jacob Christian male 48 0 0 234360 13 S
466 465 0 3 Maisner, Mr. Simon male 0 0 A/S 2816 8.05 S
467 466 0 3 Goncalves, Mr. Manuel Estanslas male 38 0 0 SOTON/O.Q. 3101306 7.05 S
468 467 0 2 Campbell, Mr. William male 0 0 239853 0 S
469 468 0 1 Smart, Mr. John Montgomery male 56 0 0 113792 26.55 S
470 469 0 3 Scanlan, Mr. James male 0 0 36209 7.725 Q
471 470 1 3 Baclini, Miss. Helene Barbara female 0.75 2 1 2666 19.2583 C
472 471 0 3 Keefe, Mr. Arthur male 0 0 323592 7.25 S
473 472 0 3 Cacic, Mr. Luka male 38 0 0 315089 8.6625 S
474 473 1 2 West, Mrs. Edwy Arthur (Ada Mary Worth) female 33 1 2 C.A. 34651 27.75 S
475 474 1 2 Jerwan, Mrs. Amin S (Marie Marthe Thuillard) female 23 0 0 SC/AH Basle 541 13.7917 D C
476 475 0 3 Strandberg, Miss. Ida Sofia female 22 0 0 7553 9.8375 S
477 476 0 1 Clifford, Mr. George Quincy male 0 0 110465 52 A14 S
478 477 0 2 Renouf, Mr. Peter Henry male 34 1 0 31027 21 S
479 478 0 3 Braund, Mr. Lewis Richard male 29 1 0 3460 7.0458 S
480 479 0 3 Karlsson, Mr. Nils August male 22 0 0 350060 7.5208 S
481 480 1 3 Hirvonen, Miss. Hildur E female 2 0 1 3101298 12.2875 S
482 481 0 3 Goodwin, Master. Harold Victor male 9 5 2 CA 2144 46.9 S
483 482 0 2 Frost, Mr. Anthony Wood "Archie" male 0 0 239854 0 S
484 483 0 3 Rouse, Mr. Richard Henry male 50 0 0 A/5 3594 8.05 S
485 484 1 3 Turkula, Mrs. (Hedwig) female 63 0 0 4134 9.5875 S
486 485 1 1 Bishop, Mr. Dickinson H male 25 1 0 11967 91.0792 B49 C
487 486 0 3 Lefebre, Miss. Jeannie female 3 1 4133 25.4667 S
488 487 1 1 Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby) female 35 1 0 19943 90 C93 S
489 488 0 1 Kent, Mr. Edward Austin male 58 0 0 11771 29.7 B37 C
490 489 0 3 Somerton, Mr. Francis William male 30 0 0 A.5. 18509 8.05 S
491 490 1 3 Coutts, Master. Eden Leslie "Neville" male 9 1 1 C.A. 37671 15.9 S
492 491 0 3 Hagland, Mr. Konrad Mathias Reiersen male 1 0 65304 19.9667 S
493 492 0 3 Windelov, Mr. Einar male 21 0 0 SOTON/OQ 3101317 7.25 S
494 493 0 1 Molson, Mr. Harry Markland male 55 0 0 113787 30.5 C30 S
495 494 0 1 Artagaveytia, Mr. Ramon male 71 0 0 PC 17609 49.5042 C
496 495 0 3 Stanley, Mr. Edward Roland male 21 0 0 A/4 45380 8.05 S
497 496 0 3 Yousseff, Mr. Gerious male 0 0 2627 14.4583 C
498 497 1 1 Eustis, Miss. Elizabeth Mussey female 54 1 0 36947 78.2667 D20 C
499 498 0 3 Shellard, Mr. Frederick William male 0 0 C.A. 6212 15.1 S
500 499 0 1 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25 1 2 113781 151.55 C22 C26 S
501 500 0 3 Svensson, Mr. Olof male 24 0 0 350035 7.7958 S
502 501 0 3 Calic, Mr. Petar male 17 0 0 315086 8.6625 S
503 502 0 3 Canavan, Miss. Mary female 21 0 0 364846 7.75 Q
504 503 0 3 O'Sullivan, Miss. Bridget Mary female 0 0 330909 7.6292 Q
505 504 0 3 Laitinen, Miss. Kristina Sofia female 37 0 0 4135 9.5875 S
506 505 1 1 Maioni, Miss. Roberta female 16 0 0 110152 86.5 B79 S
507 506 0 1 Penasco y Castellana, Mr. Victor de Satode male 18 1 0 PC 17758 108.9 C65 C
508 507 1 2 Quick, Mrs. Frederick Charles (Jane Richards) female 33 0 2 26360 26 S
509 508 1 1 Bradley, Mr. George ("George Arthur Brayton") male 0 0 111427 26.55 S
510 509 0 3 Olsen, Mr. Henry Margido male 28 0 0 C 4001 22.525 S
511 510 1 3 Lang, Mr. Fang male 26 0 0 1601 56.4958 S
512 511 1 3 Daly, Mr. Eugene Patrick male 29 0 0 382651 7.75 Q
513 512 0 3 Webber, Mr. James male 0 0 SOTON/OQ 3101316 8.05 S
514 513 1 1 McGough, Mr. James Robert male 36 0 0 PC 17473 26.2875 E25 S
515 514 1 1 Rothschild, Mrs. Martin (Elizabeth L. Barrett) female 54 1 0 PC 17603 59.4 C
516 515 0 3 Coleff, Mr. Satio male 24 0 0 349209 7.4958 S
517 516 0 1 Walker, Mr. William Anderson male 47 0 0 36967 34.0208 D46 S
518 517 1 2 Lemore, Mrs. (Amelia Milley) female 34 0 0 C.A. 34260 10.5 F33 S
519 518 0 3 Ryan, Mr. Patrick male 0 0 371110 24.15 Q
520 519 1 2 Angle, Mrs. William A (Florence "Mary" Agnes Hughes) female 36 1 0 226875 26 S
521 520 0 3 Pavlovic, Mr. Stefo male 32 0 0 349242 7.8958 S
522 521 1 1 Perreault, Miss. Anne female 30 0 0 12749 93.5 B73 S
523 522 0 3 Vovk, Mr. Janko male 22 0 0 349252 7.8958 S
524 523 0 3 Lahoud, Mr. Sarkis male 0 0 2624 7.225 C
525 524 1 1 Hippach, Mrs. Louis Albert (Ida Sophia Fischer) female 44 0 1 111361 57.9792 B18 C
526 525 0 3 Kassem, Mr. Fared male 0 0 2700 7.2292 C
527 526 0 3 Farrell, Mr. James male 40.5 0 0 367232 7.75 Q
528 527 1 2 Ridsdale, Miss. Lucy female 50 0 0 W./C. 14258 10.5 S
529 528 0 1 Farthing, Mr. John male 0 0 PC 17483 221.7792 C95 S
530 529 0 3 Salonen, Mr. Johan Werner male 39 0 0 3101296 7.925 S
531 530 0 2 Hocking, Mr. Richard George male 23 2 1 29104 11.5 S
532 531 1 2 Quick, Miss. Phyllis May female 2 1 1 26360 26 S
533 532 0 3 Toufik, Mr. Nakli male 0 0 2641 7.2292 C
534 533 0 3 Elias, Mr. Joseph Jr male 17 1 1 2690 7.2292 C
535 534 1 3 Peter, Mrs. Catherine (Catherine Rizk) female 0 2 2668 22.3583 C
536 535 0 3 Cacic, Miss. Marija female 30 0 0 315084 8.6625 S
537 536 1 2 Hart, Miss. Eva Miriam female 7 0 2 F.C.C. 13529 26.25 S
538 537 0 1 Butt, Major. Archibald Willingham male 45 0 0 113050 26.55 B38 S
539 538 1 1 LeRoy, Miss. Bertha female 30 0 0 PC 17761 106.425 C
540 539 0 3 Risien, Mr. Samuel Beard male 0 0 364498 14.5 S
541 540 1 1 Frolicher, Miss. Hedwig Margaritha female 22 0 2 13568 49.5 B39 C
542 541 1 1 Crosby, Miss. Harriet R female 36 0 2 WE/P 5735 71 B22 S
543 542 0 3 Andersson, Miss. Ingeborg Constanzia female 9 4 2 347082 31.275 S
544 543 0 3 Andersson, Miss. Sigrid Elisabeth female 11 4 2 347082 31.275 S
545 544 1 2 Beane, Mr. Edward male 32 1 0 2908 26 S
546 545 0 1 Douglas, Mr. Walter Donald male 50 1 0 PC 17761 106.425 C86 C
547 546 0 1 Nicholson, Mr. Arthur Ernest male 64 0 0 693 26 S
548 547 1 2 Beane, Mrs. Edward (Ethel Clarke) female 19 1 0 2908 26 S
549 548 1 2 Padro y Manent, Mr. Julian male 0 0 SC/PARIS 2146 13.8625 C
550 549 0 3 Goldsmith, Mr. Frank John male 33 1 1 363291 20.525 S
551 550 1 2 Davies, Master. John Morgan Jr male 8 1 1 C.A. 33112 36.75 S
552 551 1 1 Thayer, Mr. John Borland Jr male 17 0 2 17421 110.8833 C70 C
553 552 0 2 Sharp, Mr. Percival James R male 27 0 0 244358 26 S
554 553 0 3 O'Brien, Mr. Timothy male 0 0 330979 7.8292 Q
555 554 1 3 Leeni, Mr. Fahim ("Philip Zenni") male 22 0 0 2620 7.225 C
556 555 1 3 Ohman, Miss. Velin female 22 0 0 347085 7.775 S
557 556 0 1 Wright, Mr. George male 62 0 0 113807 26.55 S
558 557 1 1 Duff Gordon, Lady. (Lucille Christiana Sutherland) ("Mrs Morgan") female 48 1 0 11755 39.6 A16 C
559 558 0 1 Robbins, Mr. Victor male 0 0 PC 17757 227.525 C
560 559 1 1 Taussig, Mrs. Emil (Tillie Mandelbaum) female 39 1 1 110413 79.65 E67 S
561 560 1 3 de Messemaeker, Mrs. Guillaume Joseph (Emma) female 36 1 0 345572 17.4 S
562 561 0 3 Morrow, Mr. Thomas Rowan male 0 0 372622 7.75 Q
563 562 0 3 Sivic, Mr. Husein male 40 0 0 349251 7.8958 S
564 563 0 2 Norman, Mr. Robert Douglas male 28 0 0 218629 13.5 S
565 564 0 3 Simmons, Mr. John male 0 0 SOTON/OQ 392082 8.05 S
566 565 0 3 Meanwell, Miss. (Marion Ogden) female 0 0 SOTON/O.Q. 392087 8.05 S
567 566 0 3 Davies, Mr. Alfred J male 24 2 0 A/4 48871 24.15 S
568 567 0 3 Stoytcheff, Mr. Ilia male 19 0 0 349205 7.8958 S
569 568 0 3 Palsson, Mrs. Nils (Alma Cornelia Berglund) female 29 0 4 349909 21.075 S
570 569 0 3 Doharr, Mr. Tannous male 0 0 2686 7.2292 C
571 570 1 3 Jonsson, Mr. Carl male 32 0 0 350417 7.8542 S
572 571 1 2 Harris, Mr. George male 62 0 0 S.W./PP 752 10.5 S
573 572 1 1 Appleton, Mrs. Edward Dale (Charlotte Lamson) female 53 2 0 11769 51.4792 C101 S
574 573 1 1 Flynn, Mr. John Irwin ("Irving") male 36 0 0 PC 17474 26.3875 E25 S
575 574 1 3 Kelly, Miss. Mary female 0 0 14312 7.75 Q
576 575 0 3 Rush, Mr. Alfred George John male 16 0 0 A/4. 20589 8.05 S
577 576 0 3 Patchett, Mr. George male 19 0 0 358585 14.5 S
578 577 1 2 Garside, Miss. Ethel female 34 0 0 243880 13 S
579 578 1 1 Silvey, Mrs. William Baird (Alice Munger) female 39 1 0 13507 55.9 E44 S
580 579 0 3 Caram, Mrs. Joseph (Maria Elias) female 1 0 2689 14.4583 C
581 580 1 3 Jussila, Mr. Eiriik male 32 0 0 STON/O 2. 3101286 7.925 S
582 581 1 2 Christy, Miss. Julie Rachel female 25 1 1 237789 30 S
583 582 1 1 Thayer, Mrs. John Borland (Marian Longstreth Morris) female 39 1 1 17421 110.8833 C68 C
584 583 0 2 Downton, Mr. William James male 54 0 0 28403 26 S
585 584 0 1 Ross, Mr. John Hugo male 36 0 0 13049 40.125 A10 C
586 585 0 3 Paulner, Mr. Uscher male 0 0 3411 8.7125 C
587 586 1 1 Taussig, Miss. Ruth female 18 0 2 110413 79.65 E68 S
588 587 0 2 Jarvis, Mr. John Denzil male 47 0 0 237565 15 S
589 588 1 1 Frolicher-Stehli, Mr. Maxmillian male 60 1 1 13567 79.2 B41 C
590 589 0 3 Gilinski, Mr. Eliezer male 22 0 0 14973 8.05 S
591 590 0 3 Murdlin, Mr. Joseph male 0 0 A./5. 3235 8.05 S
592 591 0 3 Rintamaki, Mr. Matti male 35 0 0 STON/O 2. 3101273 7.125 S
593 592 1 1 Stephenson, Mrs. Walter Bertram (Martha Eustis) female 52 1 0 36947 78.2667 D20 C
594 593 0 3 Elsbury, Mr. William James male 47 0 0 A/5 3902 7.25 S
595 594 0 3 Bourke, Miss. Mary female 0 2 364848 7.75 Q
596 595 0 2 Chapman, Mr. John Henry male 37 1 0 SC/AH 29037 26 S
597 596 0 3 Van Impe, Mr. Jean Baptiste male 36 1 1 345773 24.15 S
598 597 1 2 Leitch, Miss. Jessie Wills female 0 0 248727 33 S
599 598 0 3 Johnson, Mr. Alfred male 49 0 0 LINE 0 S
600 599 0 3 Boulos, Mr. Hanna male 0 0 2664 7.225 C
601 600 1 1 Duff Gordon, Sir. Cosmo Edmund ("Mr Morgan") male 49 1 0 PC 17485 56.9292 A20 C
602 601 1 2 Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy) female 24 2 1 243847 27 S
603 602 0 3 Slabenoff, Mr. Petco male 0 0 349214 7.8958 S
604 603 0 1 Harrington, Mr. Charles H male 0 0 113796 42.4 S
605 604 0 3 Torber, Mr. Ernst William male 44 0 0 364511 8.05 S
606 605 1 1 Homer, Mr. Harry ("Mr E Haven") male 35 0 0 111426 26.55 C
607 606 0 3 Lindell, Mr. Edvard Bengtsson male 36 1 0 349910 15.55 S
608 607 0 3 Karaic, Mr. Milan male 30 0 0 349246 7.8958 S
609 608 1 1 Daniel, Mr. Robert Williams male 27 0 0 113804 30.5 S
610 609 1 2 Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue) female 22 1 2 SC/Paris 2123 41.5792 C
611 610 1 1 Shutes, Miss. Elizabeth W female 40 0 0 PC 17582 153.4625 C125 S
612 611 0 3 Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren) female 39 1 5 347082 31.275 S
613 612 0 3 Jardin, Mr. Jose Neto male 0 0 SOTON/O.Q. 3101305 7.05 S
614 613 1 3 Murphy, Miss. Margaret Jane female 1 0 367230 15.5 Q
615 614 0 3 Horgan, Mr. John male 0 0 370377 7.75 Q
616 615 0 3 Brocklebank, Mr. William Alfred male 35 0 0 364512 8.05 S
617 616 1 2 Herman, Miss. Alice female 24 1 2 220845 65 S
618 617 0 3 Danbom, Mr. Ernst Gilbert male 34 1 1 347080 14.4 S
619 618 0 3 Lobb, Mrs. William Arthur (Cordelia K Stanlick) female 26 1 0 A/5. 3336 16.1 S
620 619 1 2 Becker, Miss. Marion Louise female 4 2 1 230136 39 F4 S
621 620 0 2 Gavey, Mr. Lawrence male 26 0 0 31028 10.5 S
622 621 0 3 Yasbeck, Mr. Antoni male 27 1 0 2659 14.4542 C
623 622 1 1 Kimball, Mr. Edwin Nelson Jr male 42 1 0 11753 52.5542 D19 S
624 623 1 3 Nakid, Mr. Sahid male 20 1 1 2653 15.7417 C
625 624 0 3 Hansen, Mr. Henry Damsgaard male 21 0 0 350029 7.8542 S
626 625 0 3 Bowen, Mr. David John "Dai" male 21 0 0 54636 16.1 S
627 626 0 1 Sutton, Mr. Frederick male 61 0 0 36963 32.3208 D50 S
628 627 0 2 Kirkland, Rev. Charles Leonard male 57 0 0 219533 12.35 Q
629 628 1 1 Longley, Miss. Gretchen Fiske female 21 0 0 13502 77.9583 D9 S
630 629 0 3 Bostandyeff, Mr. Guentcho male 26 0 0 349224 7.8958 S
631 630 0 3 O'Connell, Mr. Patrick D male 0 0 334912 7.7333 Q
632 631 1 1 Barkworth, Mr. Algernon Henry Wilson male 80 0 0 27042 30 A23 S
633 632 0 3 Lundahl, Mr. Johan Svensson male 51 0 0 347743 7.0542 S
634 633 1 1 Stahelin-Maeglin, Dr. Max male 32 0 0 13214 30.5 B50 C
635 634 0 1 Parr, Mr. William Henry Marsh male 0 0 112052 0 S
636 635 0 3 Skoog, Miss. Mabel female 9 3 2 347088 27.9 S
637 636 1 2 Davis, Miss. Mary female 28 0 0 237668 13 S
638 637 0 3 Leinonen, Mr. Antti Gustaf male 32 0 0 STON/O 2. 3101292 7.925 S
639 638 0 2 Collyer, Mr. Harvey male 31 1 1 C.A. 31921 26.25 S
640 639 0 3 Panula, Mrs. Juha (Maria Emilia Ojala) female 41 0 5 3101295 39.6875 S
641 640 0 3 Thorneycroft, Mr. Percival male 1 0 376564 16.1 S
642 641 0 3 Jensen, Mr. Hans Peder male 20 0 0 350050 7.8542 S
643 642 1 1 Sagesser, Mlle. Emma female 24 0 0 PC 17477 69.3 B35 C
644 643 0 3 Skoog, Miss. Margit Elizabeth female 2 3 2 347088 27.9 S
645 644 1 3 Foo, Mr. Choong male 0 0 1601 56.4958 S
646 645 1 3 Baclini, Miss. Eugenie female 0.75 2 1 2666 19.2583 C
647 646 1 1 Harper, Mr. Henry Sleeper male 48 1 0 PC 17572 76.7292 D33 C
648 647 0 3 Cor, Mr. Liudevit male 19 0 0 349231 7.8958 S
649 648 1 1 Simonius-Blumer, Col. Oberst Alfons male 56 0 0 13213 35.5 A26 C
650 649 0 3 Willey, Mr. Edward male 0 0 S.O./P.P. 751 7.55 S
651 650 1 3 Stanley, Miss. Amy Zillah Elsie female 23 0 0 CA. 2314 7.55 S
652 651 0 3 Mitkoff, Mr. Mito male 0 0 349221 7.8958 S
653 652 1 2 Doling, Miss. Elsie female 18 0 1 231919 23 S
654 653 0 3 Kalvik, Mr. Johannes Halvorsen male 21 0 0 8475 8.4333 S
655 654 1 3 O'Leary, Miss. Hanora "Norah" female 0 0 330919 7.8292 Q
656 655 0 3 Hegarty, Miss. Hanora "Nora" female 18 0 0 365226 6.75 Q
657 656 0 2 Hickman, Mr. Leonard Mark male 24 2 0 S.O.C. 14879 73.5 S
658 657 0 3 Radeff, Mr. Alexander male 0 0 349223 7.8958 S
659 658 0 3 Bourke, Mrs. John (Catherine) female 32 1 1 364849 15.5 Q
660 659 0 2 Eitemiller, Mr. George Floyd male 23 0 0 29751 13 S
661 660 0 1 Newell, Mr. Arthur Webster male 58 0 2 35273 113.275 D48 C
662 661 1 1 Frauenthal, Dr. Henry William male 50 2 0 PC 17611 133.65 S
663 662 0 3 Badt, Mr. Mohamed male 40 0 0 2623 7.225 C
664 663 0 1 Colley, Mr. Edward Pomeroy male 47 0 0 5727 25.5875 E58 S
665 664 0 3 Coleff, Mr. Peju male 36 0 0 349210 7.4958 S
666 665 1 3 Lindqvist, Mr. Eino William male 20 1 0 STON/O 2. 3101285 7.925 S
667 666 0 2 Hickman, Mr. Lewis male 32 2 0 S.O.C. 14879 73.5 S
668 667 0 2 Butler, Mr. Reginald Fenton male 25 0 0 234686 13 S
669 668 0 3 Rommetvedt, Mr. Knud Paust male 0 0 312993 7.775 S
670 669 0 3 Cook, Mr. Jacob male 43 0 0 A/5 3536 8.05 S
671 670 1 1 Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright) female 1 0 19996 52 C126 S
672 671 1 2 Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford) female 40 1 1 29750 39 S
673 672 0 1 Davidson, Mr. Thornton male 31 1 0 F.C. 12750 52 B71 S
674 673 0 2 Mitchell, Mr. Henry Michael male 70 0 0 C.A. 24580 10.5 S
675 674 1 2 Wilhelms, Mr. Charles male 31 0 0 244270 13 S
676 675 0 2 Watson, Mr. Ennis Hastings male 0 0 239856 0 S
677 676 0 3 Edvardsson, Mr. Gustaf Hjalmar male 18 0 0 349912 7.775 S
678 677 0 3 Sawyer, Mr. Frederick Charles male 24.5 0 0 342826 8.05 S
679 678 1 3 Turja, Miss. Anna Sofia female 18 0 0 4138 9.8417 S
680 679 0 3 Goodwin, Mrs. Frederick (Augusta Tyler) female 43 1 6 CA 2144 46.9 S
681 680 1 1 Cardeza, Mr. Thomas Drake Martinez male 36 0 1 PC 17755 512.3292 B51 B53 B55 C
682 681 0 3 Peters, Miss. Katie female 0 0 330935 8.1375 Q
683 682 1 1 Hassab, Mr. Hammad male 27 0 0 PC 17572 76.7292 D49 C
684 683 0 3 Olsvigen, Mr. Thor Anderson male 20 0 0 6563 9.225 S
685 684 0 3 Goodwin, Mr. Charles Edward male 14 5 2 CA 2144 46.9 S
686 685 0 2 Brown, Mr. Thomas William Solomon male 60 1 1 29750 39 S
687 686 0 2 Laroche, Mr. Joseph Philippe Lemercier male 25 1 2 SC/Paris 2123 41.5792 C
688 687 0 3 Panula, Mr. Jaako Arnold male 14 4 1 3101295 39.6875 S
689 688 0 3 Dakic, Mr. Branko male 19 0 0 349228 10.1708 S
690 689 0 3 Fischer, Mr. Eberhard Thelander male 18 0 0 350036 7.7958 S
691 690 1 1 Madill, Miss. Georgette Alexandra female 15 0 1 24160 211.3375 B5 S
692 691 1 1 Dick, Mr. Albert Adrian male 31 1 0 17474 57 B20 S
693 692 1 3 Karun, Miss. Manca female 4 0 1 349256 13.4167 C
694 693 1 3 Lam, Mr. Ali male 0 0 1601 56.4958 S
695 694 0 3 Saad, Mr. Khalil male 25 0 0 2672 7.225 C
696 695 0 1 Weir, Col. John male 60 0 0 113800 26.55 S
697 696 0 2 Chapman, Mr. Charles Henry male 52 0 0 248731 13.5 S
698 697 0 3 Kelly, Mr. James male 44 0 0 363592 8.05 S
699 698 1 3 Mullens, Miss. Katherine "Katie" female 0 0 35852 7.7333 Q
700 699 0 1 Thayer, Mr. John Borland male 49 1 1 17421 110.8833 C68 C
701 700 0 3 Humblen, Mr. Adolf Mathias Nicolai Olsen male 42 0 0 348121 7.65 F G63 S
702 701 1 1 Astor, Mrs. John Jacob (Madeleine Talmadge Force) female 18 1 0 PC 17757 227.525 C62 C64 C
703 702 1 1 Silverthorne, Mr. Spencer Victor male 35 0 0 PC 17475 26.2875 E24 S
704 703 0 3 Barbara, Miss. Saiide female 18 0 1 2691 14.4542 C
705 704 0 3 Gallagher, Mr. Martin male 25 0 0 36864 7.7417 Q
706 705 0 3 Hansen, Mr. Henrik Juul male 26 1 0 350025 7.8542 S
707 706 0 2 Morley, Mr. Henry Samuel ("Mr Henry Marshall") male 39 0 0 250655 26 S
708 707 1 2 Kelly, Mrs. Florence "Fannie" female 45 0 0 223596 13.5 S
709 708 1 1 Calderhead, Mr. Edward Pennington male 42 0 0 PC 17476 26.2875 E24 S
710 709 1 1 Cleaver, Miss. Alice female 22 0 0 113781 151.55 S
711 710 1 3 Moubarek, Master. Halim Gonios ("William George") male 1 1 2661 15.2458 C
712 711 1 1 Mayne, Mlle. Berthe Antonine ("Mrs de Villiers") female 24 0 0 PC 17482 49.5042 C90 C
713 712 0 1 Klaber, Mr. Herman male 0 0 113028 26.55 C124 S
714 713 1 1 Taylor, Mr. Elmer Zebley male 48 1 0 19996 52 C126 S
715 714 0 3 Larsson, Mr. August Viktor male 29 0 0 7545 9.4833 S
716 715 0 2 Greenberg, Mr. Samuel male 52 0 0 250647 13 S
717 716 0 3 Soholt, Mr. Peter Andreas Lauritz Andersen male 19 0 0 348124 7.65 F G73 S
718 717 1 1 Endres, Miss. Caroline Louise female 38 0 0 PC 17757 227.525 C45 C
719 718 1 2 Troutt, Miss. Edwina Celia "Winnie" female 27 0 0 34218 10.5 E101 S
720 719 0 3 McEvoy, Mr. Michael male 0 0 36568 15.5 Q
721 720 0 3 Johnson, Mr. Malkolm Joackim male 33 0 0 347062 7.775 S
722 721 1 2 Harper, Miss. Annie Jessie "Nina" female 6 0 1 248727 33 S
723 722 0 3 Jensen, Mr. Svend Lauritz male 17 1 0 350048 7.0542 S
724 723 0 2 Gillespie, Mr. William Henry male 34 0 0 12233 13 S
725 724 0 2 Hodges, Mr. Henry Price male 50 0 0 250643 13 S
726 725 1 1 Chambers, Mr. Norman Campbell male 27 1 0 113806 53.1 E8 S
727 726 0 3 Oreskovic, Mr. Luka male 20 0 0 315094 8.6625 S
728 727 1 2 Renouf, Mrs. Peter Henry (Lillian Jefferys) female 30 3 0 31027 21 S
729 728 1 3 Mannion, Miss. Margareth female 0 0 36866 7.7375 Q
730 729 0 2 Bryhl, Mr. Kurt Arnold Gottfrid male 25 1 0 236853 26 S
731 730 0 3 Ilmakangas, Miss. Pieta Sofia female 25 1 0 STON/O2. 3101271 7.925 S
732 731 1 1 Allen, Miss. Elisabeth Walton female 29 0 0 24160 211.3375 B5 S
733 732 0 3 Hassan, Mr. Houssein G N male 11 0 0 2699 18.7875 C
734 733 0 2 Knight, Mr. Robert J male 0 0 239855 0 S
735 734 0 2 Berriman, Mr. William John male 23 0 0 28425 13 S
736 735 0 2 Troupiansky, Mr. Moses Aaron male 23 0 0 233639 13 S
737 736 0 3 Williams, Mr. Leslie male 28.5 0 0 54636 16.1 S
738 737 0 3 Ford, Mrs. Edward (Margaret Ann Watson) female 48 1 3 W./C. 6608 34.375 S
739 738 1 1 Lesurer, Mr. Gustave J male 35 0 0 PC 17755 512.3292 B101 C
740 739 0 3 Ivanoff, Mr. Kanio male 0 0 349201 7.8958 S
741 740 0 3 Nankoff, Mr. Minko male 0 0 349218 7.8958 S
742 741 1 1 Hawksford, Mr. Walter James male 0 0 16988 30 D45 S
743 742 0 1 Cavendish, Mr. Tyrell William male 36 1 0 19877 78.85 C46 S
744 743 1 1 Ryerson, Miss. Susan Parker "Suzette" female 21 2 2 PC 17608 262.375 B57 B59 B63 B66 C
745 744 0 3 McNamee, Mr. Neal male 24 1 0 376566 16.1 S
746 745 1 3 Stranden, Mr. Juho male 31 0 0 STON/O 2. 3101288 7.925 S
747 746 0 1 Crosby, Capt. Edward Gifford male 70 1 1 WE/P 5735 71 B22 S
748 747 0 3 Abbott, Mr. Rossmore Edward male 16 1 1 C.A. 2673 20.25 S
749 748 1 2 Sinkkonen, Miss. Anna female 30 0 0 250648 13 S
750 749 0 1 Marvin, Mr. Daniel Warner male 19 1 0 113773 53.1 D30 S
751 750 0 3 Connaghton, Mr. Michael male 31 0 0 335097 7.75 Q
752 751 1 2 Wells, Miss. Joan female 4 1 1 29103 23 S
753 752 1 3 Moor, Master. Meier male 6 0 1 392096 12.475 E121 S
754 753 0 3 Vande Velde, Mr. Johannes Joseph male 33 0 0 345780 9.5 S
755 754 0 3 Jonkoff, Mr. Lalio male 23 0 0 349204 7.8958 S
756 755 1 2 Herman, Mrs. Samuel (Jane Laver) female 48 1 2 220845 65 S
757 756 1 2 Hamalainen, Master. Viljo male 0.67 1 1 250649 14.5 S
758 757 0 3 Carlsson, Mr. August Sigfrid male 28 0 0 350042 7.7958 S
759 758 0 2 Bailey, Mr. Percy Andrew male 18 0 0 29108 11.5 S
760 759 0 3 Theobald, Mr. Thomas Leonard male 34 0 0 363294 8.05 S
761 760 1 1 Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards) female 33 0 0 110152 86.5 B77 S
762 761 0 3 Garfirth, Mr. John male 0 0 358585 14.5 S
763 762 0 3 Nirva, Mr. Iisakki Antino Aijo male 41 0 0 SOTON/O2 3101272 7.125 S
764 763 1 3 Barah, Mr. Hanna Assi male 20 0 0 2663 7.2292 C
765 764 1 1 Carter, Mrs. William Ernest (Lucile Polk) female 36 1 2 113760 120 B96 B98 S
766 765 0 3 Eklund, Mr. Hans Linus male 16 0 0 347074 7.775 S
767 766 1 1 Hogeboom, Mrs. John C (Anna Andrews) female 51 1 0 13502 77.9583 D11 S
768 767 0 1 Brewe, Dr. Arthur Jackson male 0 0 112379 39.6 C
769 768 0 3 Mangan, Miss. Mary female 30.5 0 0 364850 7.75 Q
770 769 0 3 Moran, Mr. Daniel J male 1 0 371110 24.15 Q
771 770 0 3 Gronnestad, Mr. Daniel Danielsen male 32 0 0 8471 8.3625 S
772 771 0 3 Lievens, Mr. Rene Aime male 24 0 0 345781 9.5 S
773 772 0 3 Jensen, Mr. Niels Peder male 48 0 0 350047 7.8542 S
774 773 0 2 Mack, Mrs. (Mary) female 57 0 0 S.O./P.P. 3 10.5 E77 S
775 774 0 3 Elias, Mr. Dibo male 0 0 2674 7.225 C
776 775 1 2 Hocking, Mrs. Elizabeth (Eliza Needs) female 54 1 3 29105 23 S
777 776 0 3 Myhrman, Mr. Pehr Fabian Oliver Malkolm male 18 0 0 347078 7.75 S
778 777 0 3 Tobin, Mr. Roger male 0 0 383121 7.75 F38 Q
779 778 1 3 Emanuel, Miss. Virginia Ethel female 5 0 0 364516 12.475 S
780 779 0 3 Kilgannon, Mr. Thomas J male 0 0 36865 7.7375 Q
781 780 1 1 Robert, Mrs. Edward Scott (Elisabeth Walton McMillan) female 43 0 1 24160 211.3375 B3 S
782 781 1 3 Ayoub, Miss. Banoura female 13 0 0 2687 7.2292 C
783 782 1 1 Dick, Mrs. Albert Adrian (Vera Gillespie) female 17 1 0 17474 57 B20 S
784 783 0 1 Long, Mr. Milton Clyde male 29 0 0 113501 30 D6 S
785 784 0 3 Johnston, Mr. Andrew G male 1 2 W./C. 6607 23.45 S
786 785 0 3 Ali, Mr. William male 25 0 0 SOTON/O.Q. 3101312 7.05 S
787 786 0 3 Harmer, Mr. Abraham (David Lishin) male 25 0 0 374887 7.25 S
788 787 1 3 Sjoblom, Miss. Anna Sofia female 18 0 0 3101265 7.4958 S
789 788 0 3 Rice, Master. George Hugh male 8 4 1 382652 29.125 Q
790 789 1 3 Dean, Master. Bertram Vere male 1 1 2 C.A. 2315 20.575 S
791 790 0 1 Guggenheim, Mr. Benjamin male 46 0 0 PC 17593 79.2 B82 B84 C
792 791 0 3 Keane, Mr. Andrew "Andy" male 0 0 12460 7.75 Q
793 792 0 2 Gaskell, Mr. Alfred male 16 0 0 239865 26 S
794 793 0 3 Sage, Miss. Stella Anna female 8 2 CA. 2343 69.55 S
795 794 0 1 Hoyt, Mr. William Fisher male 0 0 PC 17600 30.6958 C
796 795 0 3 Dantcheff, Mr. Ristiu male 25 0 0 349203 7.8958 S
797 796 0 2 Otter, Mr. Richard male 39 0 0 28213 13 S
798 797 1 1 Leader, Dr. Alice (Farnham) female 49 0 0 17465 25.9292 D17 S
799 798 1 3 Osman, Mrs. Mara female 31 0 0 349244 8.6833 S
800 799 0 3 Ibrahim Shawah, Mr. Yousseff male 30 0 0 2685 7.2292 C
801 800 0 3 Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert) female 30 1 1 345773 24.15 S
802 801 0 2 Ponesell, Mr. Martin male 34 0 0 250647 13 S
803 802 1 2 Collyer, Mrs. Harvey (Charlotte Annie Tate) female 31 1 1 C.A. 31921 26.25 S
804 803 1 1 Carter, Master. William Thornton II male 11 1 2 113760 120 B96 B98 S
805 804 1 3 Thomas, Master. Assad Alexander male 0.42 0 1 2625 8.5167 C
806 805 1 3 Hedman, Mr. Oskar Arvid male 27 0 0 347089 6.975 S
807 806 0 3 Johansson, Mr. Karl Johan male 31 0 0 347063 7.775 S
808 807 0 1 Andrews, Mr. Thomas Jr male 39 0 0 112050 0 A36 S
809 808 0 3 Pettersson, Miss. Ellen Natalia female 18 0 0 347087 7.775 S
810 809 0 2 Meyer, Mr. August male 39 0 0 248723 13 S
811 810 1 1 Chambers, Mrs. Norman Campbell (Bertha Griggs) female 33 1 0 113806 53.1 E8 S
812 811 0 3 Alexander, Mr. William male 26 0 0 3474 7.8875 S
813 812 0 3 Lester, Mr. James male 39 0 0 A/4 48871 24.15 S
814 813 0 2 Slemen, Mr. Richard James male 35 0 0 28206 10.5 S
815 814 0 3 Andersson, Miss. Ebba Iris Alfrida female 6 4 2 347082 31.275 S
816 815 0 3 Tomlin, Mr. Ernest Portage male 30.5 0 0 364499 8.05 S
817 816 0 1 Fry, Mr. Richard male 0 0 112058 0 B102 S
818 817 0 3 Heininen, Miss. Wendla Maria female 23 0 0 STON/O2. 3101290 7.925 S
819 818 0 2 Mallet, Mr. Albert male 31 1 1 S.C./PARIS 2079 37.0042 C
820 819 0 3 Holm, Mr. John Fredrik Alexander male 43 0 0 C 7075 6.45 S
821 820 0 3 Skoog, Master. Karl Thorsten male 10 3 2 347088 27.9 S
822 821 1 1 Hays, Mrs. Charles Melville (Clara Jennings Gregg) female 52 1 1 12749 93.5 B69 S
823 822 1 3 Lulic, Mr. Nikola male 27 0 0 315098 8.6625 S
824 823 0 1 Reuchlin, Jonkheer. John George male 38 0 0 19972 0 S
825 824 1 3 Moor, Mrs. (Beila) female 27 0 1 392096 12.475 E121 S
826 825 0 3 Panula, Master. Urho Abraham male 2 4 1 3101295 39.6875 S
827 826 0 3 Flynn, Mr. John male 0 0 368323 6.95 Q
828 827 0 3 Lam, Mr. Len male 0 0 1601 56.4958 S
829 828 1 2 Mallet, Master. Andre male 1 0 2 S.C./PARIS 2079 37.0042 C
830 829 1 3 McCormack, Mr. Thomas Joseph male 0 0 367228 7.75 Q
831 830 1 1 Stone, Mrs. George Nelson (Martha Evelyn) female 62 0 0 113572 80 B28
832 831 1 3 Yasbeck, Mrs. Antoni (Selini Alexander) female 15 1 0 2659 14.4542 C
833 832 1 2 Richards, Master. George Sibley male 0.83 1 1 29106 18.75 S
834 833 0 3 Saad, Mr. Amin male 0 0 2671 7.2292 C
835 834 0 3 Augustsson, Mr. Albert male 23 0 0 347468 7.8542 S
836 835 0 3 Allum, Mr. Owen George male 18 0 0 2223 8.3 S
837 836 1 1 Compton, Miss. Sara Rebecca female 39 1 1 PC 17756 83.1583 E49 C
838 837 0 3 Pasic, Mr. Jakob male 21 0 0 315097 8.6625 S
839 838 0 3 Sirota, Mr. Maurice male 0 0 392092 8.05 S
840 839 1 3 Chip, Mr. Chang male 32 0 0 1601 56.4958 S
841 840 1 1 Marechal, Mr. Pierre male 0 0 11774 29.7 C47 C
842 841 0 3 Alhomaki, Mr. Ilmari Rudolf male 20 0 0 SOTON/O2 3101287 7.925 S
843 842 0 2 Mudd, Mr. Thomas Charles male 16 0 0 S.O./P.P. 3 10.5 S
844 843 1 1 Serepeca, Miss. Augusta female 30 0 0 113798 31 C
845 844 0 3 Lemberopolous, Mr. Peter L male 34.5 0 0 2683 6.4375 C
846 845 0 3 Culumovic, Mr. Jeso male 17 0 0 315090 8.6625 S
847 846 0 3 Abbing, Mr. Anthony male 42 0 0 C.A. 5547 7.55 S
848 847 0 3 Sage, Mr. Douglas Bullen male 8 2 CA. 2343 69.55 S
849 848 0 3 Markoff, Mr. Marin male 35 0 0 349213 7.8958 C
850 849 0 2 Harper, Rev. John male 28 0 1 248727 33 S
851 850 1 1 Goldenberg, Mrs. Samuel L (Edwiga Grabowska) female 1 0 17453 89.1042 C92 C
852 851 0 3 Andersson, Master. Sigvard Harald Elias male 4 4 2 347082 31.275 S
853 852 0 3 Svensson, Mr. Johan male 74 0 0 347060 7.775 S
854 853 0 3 Boulos, Miss. Nourelain female 9 1 1 2678 15.2458 C
855 854 1 1 Lines, Miss. Mary Conover female 16 0 1 PC 17592 39.4 D28 S
856 855 0 2 Carter, Mrs. Ernest Courtenay (Lilian Hughes) female 44 1 0 244252 26 S
857 856 1 3 Aks, Mrs. Sam (Leah Rosen) female 18 0 1 392091 9.35 S
858 857 1 1 Wick, Mrs. George Dennick (Mary Hitchcock) female 45 1 1 36928 164.8667 S
859 858 1 1 Daly, Mr. Peter Denis male 51 0 0 113055 26.55 E17 S
860 859 1 3 Baclini, Mrs. Solomon (Latifa Qurban) female 24 0 3 2666 19.2583 C
861 860 0 3 Razi, Mr. Raihed male 0 0 2629 7.2292 C
862 861 0 3 Hansen, Mr. Claus Peter male 41 2 0 350026 14.1083 S
863 862 0 2 Giles, Mr. Frederick Edward male 21 1 0 28134 11.5 S
864 863 1 1 Swift, Mrs. Frederick Joel (Margaret Welles Barron) female 48 0 0 17466 25.9292 D17 S
865 864 0 3 Sage, Miss. Dorothy Edith "Dolly" female 8 2 CA. 2343 69.55 S
866 865 0 2 Gill, Mr. John William male 24 0 0 233866 13 S
867 866 1 2 Bystrom, Mrs. (Karolina) female 42 0 0 236852 13 S
868 867 1 2 Duran y More, Miss. Asuncion female 27 1 0 SC/PARIS 2149 13.8583 C
869 868 0 1 Roebling, Mr. Washington Augustus II male 31 0 0 PC 17590 50.4958 A24 S
870 869 0 3 van Melkebeke, Mr. Philemon male 0 0 345777 9.5 S
871 870 1 3 Johnson, Master. Harold Theodor male 4 1 1 347742 11.1333 S
872 871 0 3 Balkic, Mr. Cerin male 26 0 0 349248 7.8958 S
873 872 1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47 1 1 11751 52.5542 D35 S
874 873 0 1 Carlsson, Mr. Frans Olof male 33 0 0 695 5 B51 B53 B55 S
875 874 0 3 Vander Cruyssen, Mr. Victor male 47 0 0 345765 9 S
876 875 1 2 Abelson, Mrs. Samuel (Hannah Wizosky) female 28 1 0 P/PP 3381 24 C
877 876 1 3 Najib, Miss. Adele Kiamie "Jane" female 15 0 0 2667 7.225 C
878 877 0 3 Gustafsson, Mr. Alfred Ossian male 20 0 0 7534 9.8458 S
879 878 0 3 Petroff, Mr. Nedelio male 19 0 0 349212 7.8958 S
880 879 0 3 Laleff, Mr. Kristo male 0 0 349217 7.8958 S
881 880 1 1 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56 0 1 11767 83.1583 C50 C
882 881 1 2 Shelley, Mrs. William (Imanita Parrish Hall) female 25 0 1 230433 26 S
883 882 0 3 Markun, Mr. Johann male 33 0 0 349257 7.8958 S
884 883 0 3 Dahlberg, Miss. Gerda Ulrika female 22 0 0 7552 10.5167 S
885 884 0 2 Banfield, Mr. Frederick James male 28 0 0 C.A./SOTON 34068 10.5 S
886 885 0 3 Sutehall, Mr. Henry Jr male 25 0 0 SOTON/OQ 392076 7.05 S
887 886 0 3 Rice, Mrs. William (Margaret Norton) female 39 0 5 382652 29.125 Q
888 887 0 2 Montvila, Rev. Juozas male 27 0 0 211536 13 S
889 888 1 1 Graham, Miss. Margaret Edith female 19 0 0 112053 30 B42 S
890 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female 1 2 W./C. 6607 23.45 S
891 890 1 1 Behr, Mr. Karl Howell male 26 0 0 111369 30 C148 C
892 891 0 3 Dooley, Mr. Patrick male 32 0 0 370376 7.75 Q

40
alexandrov_dmitrii_lab_4/lab4.py Normal file
View File

@ -0,0 +1,40 @@
from scipy.cluster import hierarchy
import pandas as pd
from matplotlib import pyplot as plt
def start():
data = pd.read_csv('sberbank_data.csv', index_col='id')
x = data[['full_sq', 'price_doc']]
plt.figure(1, figsize=(16, 9))
plt.title('Дендрограмма кластеризации цен')
prices = [0, 0, 0, 0]
for ind, val in x.iterrows():
val = val['price_doc'] / val['full_sq']
if val < 100000:
prices[0] = prices[0] + 1
elif val < 300000:
prices[1] = prices[1] + 1
elif val < 500000:
prices[2] = prices[2] + 1
else:
prices[3] = prices[3] + 1
print('Результаты подчсёта ручного распределения:')
print('низких цен:'+str(prices[0]))
print('средних цен:'+str(prices[1]))
print('высоких цен:'+str(prices[2]))
print('премиальных цен:'+str(prices[3]))
hierarchy.dendrogram(hierarchy.linkage(x, method='single'),
truncate_mode='lastp',
p=15,
orientation='top',
leaf_rotation=90,
leaf_font_size=8,
show_contracted=True)
plt.show()
start()

27
alexandrov_dmitrii_lab_4/readme.md Normal file
View File

@ -0,0 +1,27 @@
### Задание
Использовать метод кластеризации по варианту для выбранных данных по варианту, самостоятельно сформулировав задачу.
Интерпретировать результаты и оценить, насколько хорошо он подходит для
решения сформулированной вами задачи.
Вариант 1: dendrogram
Была сформулирована следующая задача: необходимо разбить записи на кластеры в зависимости от цен и площади.
### Запуск программы
Файл lab4.py содержит и запускает программу, аргументов и настройки ~~вроде~~ не требует.
### Описание программы
Программа считывает цены и площади из файла статистики сбербанка по рынку недвижимости.
Поскольку по заданию требуется оценить машинную кластеризацию, для сравнения программа подсчитывает и выводит в консоль количество записей в каждом из выделенных вручную классов цен.
Далее программа кластеризует данные с помощью алгоритма ближайших точек (на другие памяти нету) и выводит дендрограмму на основе кластеризации.
Выводимая дендрограмма ограничена 15 последними (верхними) объединениями.
### Результаты тестирования
По результатам тестирования, можно сказать следующее:
* Последние объединения в дендрограмме - объединения выбросов с 'основным' кластером, то есть 10-20 записей с кластером с более чем 28000 записями.
* Это правильная информация, так как ручная классификация показывает, что премиальных (аномально больших) цен как раз порядка 20, остальные относятся к другим классам.
* Поскольку в имеющихся данных нет ограничений по ценам, выбросы аномально высоких цен при использовании данного алгоритма формируют отдельные кластеры, что негативно сказывается на наглядности.
* Ценовое ограничение также не дало положительнх результатов: снова сформировался 'основной' кластер, с которым последними объединялись отдельные значения.
* Значит, сам алгоритм не эффективен.
Итого: Алгоритм ближайших точек слишком чувствителен к выбросам, поэтому можно признать его неэффективным для необработанных данных. Дендрограмма как средство визуализации скорее уступает по наглядности диаграмме рассеяния.

28896
alexandrov_dmitrii_lab_4/sberbank_data.csv Normal file
View File

File diff suppressed because one or more lines are too long

48
alexandrov_dmitrii_lab_5/lab5.py Normal file
View File

@ -0,0 +1,48 @@
from matplotlib import pyplot as plt
from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import Pipeline
import pandas as pd
def start():
data = pd.read_csv('sberbank_data.csv', index_col='id')
x = data[['timestamp', 'full_sq', 'floor', 'max_floor', 'build_year', 'num_room', 'material', 'kremlin_km']]
y = data[['price_doc']]
x = x.replace('NA', 0)
x.fillna(0, inplace=True)
col_date = []
for val in x['timestamp']:
col_date.append(val.split('-', 1)[0])
x = x.drop(columns='timestamp')
x['timestamp'] = col_date
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.01, random_state=42)
poly = Pipeline([('poly', PolynomialFeatures(degree=3)),
('linear', LinearRegression())])
poly.fit(x_train, y_train)
y_mean = y['price_doc'].mean()
y_predicted = poly.predict(x_test)
for i, n in enumerate(y_predicted):
if n < 10000:
y_predicted[i] = y_mean
print('Оценка обучения:')
print(metrics.r2_score(y_test, y_predicted))
plt.figure(1, figsize=(16, 9))
plt.title('Сравнение результатов обучения')
plt.scatter(x=[i for i in range(len(y_test))], y=y_test, c='g', s=5)
plt.scatter(x=[i for i in range(len(y_test))], y=y_predicted, c='r', s=5)
plt.show()
start()

36
alexandrov_dmitrii_lab_5/readme.md Normal file
View File

@ -0,0 +1,36 @@
### Задание
Использовать регрессию по варианту для выбранных данных по варианту, самостоятельно сформулировав задачу.
Интерпретировать результаты и оценить, насколько хорошо она подходит для
решения сформулированной вами задачи.
Вариант 1: полиномиальная регрессия
Была сформулирована следующая задача: необходимо предсказывать стоимость жилья по избранным признакам при помощи регрессии.
### Запуск программы
Файл lab5.py содержит и запускает программу, аргументов и настройки ~~вроде~~ не требует.
### Описание программы
Программа считывает цены на жильё как выходные данные и следующие данные как входные: год размещения объявления, площадь, этаж, количество этажей, год постройки, количество комнат, материал, расстояние до кремля (условного центра).
Далее она обрабатывает данные (цифровизирует нулевые данные), оставляет только год объявления.
После обработки программа делит данные на 99% обучающего материала и 1% тестового и обучает модель полиномиальной регрессии со степенью 3.
Далее модель генерирует набор предсказаний на основе тестовых входных данных. Эти предсказания обрабатываются: убираются отрицательные цены.
Далее программа оценивает предсказания по коэффициенту детерминации и выводит результат в консоль. А также показывает диаграммы рассеяния для действительных (зелёные точки) и предсказанных (красные точки) цен.
### Результаты тестирования
По результатам тестирования, можно сказать следующее:
* Полные данные алгоритм обрабатывает плохо, поэтому было необходимо было выбирать наиболее значимые признаки.
* В зависимости от данных, разные степени регрессии дают разный результат. В общем случае обычная линейная регрессия давала коэффициент около 0.3. При добавлении же степеней полиномиальная регрессия выдавала выбросные значения цен: например, -300 миллионов, что негативно сказывалось на результате.
* Для того, чтобы явно выбросные результаты не портили оценку (коэффициент соответственно становился -1000) эти выбросные значения заменялись на средние.
* Опытным путём было найдено, что наилучшие результаты (коэффициент 0.54) показывает степень 3.
* Результат 0.54 - наилучший результат - можно назвать неприемлимым: только в половине случаев предсказанная цена условно похожа на действительную.
* Возможно, включением большего количества признаков и использованием других моделей (линейная, например, не давала выбросов) удастся решить проблему.
Пример консольного вывода:
>Оценка обучения:
>
>0.5390648784908953
Итого: Алгоритм можно привести к некоторой эффективности, однако для конкретно этих данных он не подходит. Лучше попытаться найти другую модель регрессии.

28896
alexandrov_dmitrii_lab_5/sberbank_data.csv Normal file
View File

File diff suppressed because one or more lines are too long

76
alexandrov_dmitrii_lab_6/lab6.py Normal file
View File

@ -0,0 +1,76 @@
from matplotlib import pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
import pandas as pd
import numpy as np
data = pd.read_csv('sberbank_data.csv', index_col='id')
x = data[['timestamp', 'full_sq', 'floor', 'max_floor', 'build_year', 'num_room', 'material', 'kremlin_km']]
x = x.replace('NA', 0)
x.fillna(0, inplace=True)
col_date = []
for val in x['timestamp']:
col_date.append(val.split('-', 1)[0])
x = x.drop(columns='timestamp')
x['timestamp'] = col_date
y = []
for val in data['price_doc']:
if val < 1500000:
y.append('low')
elif val < 3000000:
y.append('medium')
elif val < 5500000:
y.append('high')
elif val < 10000000:
y.append('premium')
else:
y.append('oligarch')
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.01, random_state=42)
min_scores = []
med_scores = []
max_scores = []
def do_test(iters_num):
global x_train, x_test, y_train, y_test, min_scores, med_scores, max_scores
print("Testing iterations number "+str(iters_num)+":")
scores = []
for i in range(10):
neuro = MLPClassifier(max_iter=200)
neuro.fit(x_train, y_train)
scr = neuro.score(x_test, y_test)
print("res"+str(i+1)+": "+str(scr))
scores.append(scr)
print("Medium result: "+str(np.mean(scores)))
min_scores.append(np.min(scores))
med_scores.append(np.mean(scores))
max_scores.append(np.max(scores))
def start():
global min_scores, med_scores, max_scores
iter_nums = [200, 400, 600, 800, 1000]
for num in iter_nums:
do_test(num)
plt.figure(1, figsize=(16, 9))
plt.plot(iter_nums, min_scores, c='r')
plt.plot(iter_nums, med_scores, c='b')
plt.plot(iter_nums, max_scores, c='b')
plt.show()
start()

149
alexandrov_dmitrii_lab_6/readme.md Normal file
View File

@ -0,0 +1,149 @@
### Задание
Использовать нейронную сеть по варианту для выбранных данных по варианту, самостоятельно сформулировав задачу.
Интерпретировать результаты и оценить, насколько хорошо она подходит для
решения сформулированной вами задачи.
Вариант 1: MLPClassifier
Была сформулирована следующая задача: необходимо классифицировать жильё по стоимости на основе избранных признаков при помощи нейронной сети.
### Запуск программы
Файл lab6.py содержит и запускает программу, аргументов и настройки ~~вроде~~ не требует.
### Описание программы
Программа считывает цены на жильё как выходные данные и следующие данные как входные: год размещения объявления, площадь, этаж, количество этажей, год постройки, количество комнат, материал, расстояние до кремля (условного центра).
Далее она обрабатывает данные (цифровизирует нулевые данные), оставляет только год объявления. Цены распределяются по пяти классам.
После обработки программа делит данные на 99% обучающего материала и 1% тестового.
Эти данные обрабатываются по 10 раз для идентичных моделей нейронных сетей, использующих метод градиентного спуска "adam", с разной настройкой максимального количества поколений: 200, 400, 600, 800, 1000.
Считаются оценка модели. Для каждой модели запоминаются минимальный, максимальный и средний результаты. В консоль выводятся все результаты.
В конце программа показывает графики зависимости результатов от максимального количества поколений модели.
### Результаты тестирования
По результатам тестирования, можно сказать следующее:
* В общем, модель даёт средний результат в районе 40-50% точности, что недостаточно.
* Увеличение максимального количества поколений влияет сильнее всего на минимальные оценки, сужая разброс точности.
* Нельзя сказать, что увеличение максимального количества поколений сильно улучшит модель: максимум на 10% точности.
Пример консольного вывода:
>Testing iterations number 200:
>
>res1: 0.3806228373702422
>
>res2: 0.6055363321799307
>
>res3: 0.4809688581314879
>
>res4: 0.4913494809688581
>
>res5: 0.4844290657439446
>
>res6: 0.2975778546712803
>
>res7: 0.48788927335640137
>
>res8: 0.06228373702422145
>
>res9: 0.6193771626297578
>
>res10: 0.47750865051903113
>
>Medium result: 0.4387543252595155
>
>Testing iterations number 400:
>
>res1: 0.6124567474048442
>
>res2: 0.4290657439446367
>
>res3: 0.3217993079584775
>
>res4: 0.5467128027681661
>
>res5: 0.48788927335640137
>
>res6: 0.40484429065743943
>
>res7: 0.6020761245674741
>
>res8: 0.4186851211072664
>
>res9: 0.42214532871972316
>
>res10: 0.370242214532872
>
>Medium result: 0.46159169550173
>
>Testing iterations number 600:
>
>res1: 0.4359861591695502
>
>res2: 0.2560553633217993
>
>res3: 0.5363321799307958
>
>res4: 0.5778546712802768
>
>res5: 0.35986159169550175
>
>res6: 0.356401384083045
>
>res7: 0.49480968858131485
>
>res8: 0.5121107266435986
>
>res9: 0.5224913494809689
>
>res10: 0.5190311418685121
>
>Medium result: 0.4570934256055363
>
>Testing iterations number 800:
>
>res1: 0.25951557093425603
>
>res2: 0.4083044982698962
>
>res3: 0.5224913494809689
>
>res4: 0.5986159169550173
>
>res5: 0.24567474048442905
>
>res6: 0.4013840830449827
>
>res7: 0.21453287197231835
>
>res8: 0.4671280276816609
>
>res9: 0.40484429065743943
>
>res10: 0.38408304498269896
>
>Medium result: 0.3906574394463667
>
>Testing iterations number 1000:
>
>res1: 0.4186851211072664
>
>res2: 0.5017301038062284
>
>res3: 0.5121107266435986
>
>res4: 0.3806228373702422
>
>res5: 0.44982698961937717
>
>res6: 0.5986159169550173
>
>res7: 0.5570934256055363
>
>res8: 0.4290657439446367
>
>res9: 0.32525951557093424
>
>res10: 0.41522491349480967
>
>Medium result: 0.4588235294117647
Итого: Для отобранных данных нейронная модель с методом градиентного спуска "adam" показала себя не лучшим образом. Возможно, другие методы могут выдать лучшие результаты, либо необходима более обширная модификация модели.

28896
alexandrov_dmitrii_lab_6/sberbank_data.csv Normal file
View File

File diff suppressed because one or more lines are too long

2795
alexandrov_dmitrii_lab_7/data.txt Normal file
View File

File diff suppressed because it is too large Load Diff

96
alexandrov_dmitrii_lab_7/lab7.py Normal file
View File

@ -0,0 +1,96 @@
import numpy as np
from keras_preprocessing.sequence import pad_sequences
from keras_preprocessing.text import Tokenizer
from keras.models import Sequential
from keras.layers import Dense, LSTM, Embedding, Dropout
from keras.callbacks import ModelCheckpoint
def recreate_model(predictors, labels, model, filepath, epoch_num):
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
append_epochs(predictors, labels, model, epoch_num)
def append_epochs(predictors, labels, model, filepath, epoch_num):
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
desired_callbacks = [checkpoint]
model.fit(predictors, labels, epochs=epoch_num, verbose=1, callbacks=desired_callbacks)
def generate_text(tokenizer, seed_text, next_words, model, max_seq_length):
for _ in range(next_words):
token_list = tokenizer.texts_to_sequences([seed_text])[0]
token_list = pad_sequences([token_list], maxlen=max_seq_length - 1, padding='pre')
predicted = np.argmax(model.predict(token_list), axis=-1)
output_word = ""
for word, index in tokenizer.word_index.items():
if index == predicted:
output_word = word
break
seed_text += " " + output_word
return seed_text
def start():
flag = -1
while flag < 1 or flag > 2:
flag = int(input("Select model and text (1 - eng, 2 - ru): "))
if flag == 1:
file = open("data.txt").read()
filepath = "model_eng.hdf5"
elif flag == 2:
file = open("rus_data.txt").read()
filepath = "model_rus.hdf5"
else:
exit(1)
tokenizer = Tokenizer()
tokenizer.fit_on_texts([file])
words_count = len(tokenizer.word_index) + 1
input_sequences = []
for line in file.split('\n'):
token_list = tokenizer.texts_to_sequences([line])[0]
for i in range(1, len(token_list)):
n_gram_sequence = token_list[:i + 1]
input_sequences.append(n_gram_sequence)
max_seq_length = max([len(x) for x in input_sequences])
input_sequences = pad_sequences(input_sequences, maxlen=max_seq_length, padding='pre')
predictors, labels = input_sequences[:, :-1], input_sequences[:, -1]
model = Sequential()
model.add(Embedding(words_count, 100, input_length=max_seq_length - 1))
model.add(LSTM(150))
model.add(Dropout(0.15))
model.add(Dense(words_count, activation='softmax'))
flag = input("Do you want to recreate the model ? (print yes): ")
if flag == 'yes':
flag = input("Are you sure? (print yes): ")
if flag == 'yes':
num = int(input("Select number of epoch: "))
if 0 < num < 100:
recreate_model(predictors, labels, model, filepath, num)
model.load_weights(filepath)
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
flag = input("Do you want to train the model ? (print yes): ")
if flag == 'yes':
flag = input("Are you sure? (print yes): ")
if flag == 'yes':
num = int(input("Select number of epoch: "))
if 0 < num < 100:
append_epochs(predictors, labels, model, filepath, num)
flag = 'y'
while flag == 'y':
seed = input("Enter seed: ")
print(generate_text(tokenizer, seed, 25, model, max_seq_length))
flag = input("Continue? (print \'y\'): ")
start()

BIN
alexandrov_dmitrii_lab_7/model_eng.hdf5 Normal file
View File

Binary file not shown.

BIN
alexandrov_dmitrii_lab_7/model_rus.hdf5 Normal file
View File

Binary file not shown.

49
alexandrov_dmitrii_lab_7/readme.md Normal file
View File

@ -0,0 +1,49 @@
### Задание
Выбрать художественный текст(четные варианты – русскоязычный, нечетные – англоязычный)и обучить на нем рекуррентную нейронную сеть для решения задачи генерации. Подобрать архитектуру и параметры так, чтобы приблизиться к максимально осмысленному результату. Далее разбиться на пары четный-нечетный вариант, обменяться разработанными сетями и проверить, как архитектура товарища справляется с вашим текстом.
Вариант 1: первостепенно - английский текст. Кооперироваться, впрочем, не с кем.
### Запуск программы
Файл lab7.py содержит и запускает программу, аргументов и настройки ~~вроде~~ не требует.
### Описание программы
Программа представляет собой консольное приложение-инструмент для работы с моделями. Она может создавать и обучать однородные модели для разных текстов.
В файлах хранятся два текста: англоязычный data.txt (Остров сокровищ) и русскоязычный rus_data.txt (Хоббит). Также там хранятся две сохранённые обученные модели:
* model_eng - модель, обученная на английском тексте. На текущий момент 27 эпох обучения.
* model_rus - модель, обученная на русском тексте. На текущий момент 12 эпох обучения.
Обучение проходило 1 день.
В программе необходимо выбрать загружаемый текст и соответствующую модель, в данный момент подключается русскоязычная модель.
Программа содержит методы пересоздания модели и дообучения модели (передаётся модель и количество эпох дообучения). Оба метода отключены и могут быть подключены обратно при необходимости.
После возможных пересоздания и дообучения моделей программа запрашивает текст-кодовое слово, которое модели будет необходимо продолжить, сгенерировав свой текст.
Сама модель имеет следующую архитектуру:
* слой, преобразующий слова в векторы плотности, Embedding с входом, равным числу слов, с выходом 100, и с длиной ввода, равной длине максимального слова.
* слой с блоками долгой краткосрочной памятью, составляющая рекуррентную сеть, LSTM со 150 блоками.
* слой, задающий степень разрыва нейронных связей между соседними слоями, Dropout с процентом разрыва 15.
* слой вычисления взвешенных сумм Dense с числом нейронов, равным числу слов в тексте и функцией активации 'softmax'
### Результаты тестирования
По результатам дневного обучения можно сказать следующее:
Модель успешно генерирует бессмысленные последовательности слов, которые либо состоят из обрывков фраз, либо случайно (но достаточно часто) складываются в осмысленные словосочетания, но не более.
Примеры генераций (первое слово - код генерации):
Модель, обученная на 'Острове сокровищ', 27 эпох обучения:
>ship that he said with the buccaneers a gentleman and neither can read and figure but what is it anyway ah 'deposed' that's it is a
>
>chest said the doctor touching the black spot mind by the arm who is the ship there's long john now you are the first that were
>
>silver said the doctor if you can get the treasure you can find the ship there's been a man that has lost his score out he
Модель, обученная на 'Хоббите', 12 эпох обучения:
>дракон и тут они услыхали про смога он понял что он стал видел и разозлился как слоны у гэндальфа хороши но все это было бы он
>
>поле он не мог сообразить что он делал то в живых и слышал бильбо как раз доедал пуще прежнего а бильбо все таки уж не мог
>
>паук направился к нему толстому из свертков они добрались до рассвета и даже дальше не останавливаясь а именно что гоблины обидело бильбо они не мог ничего
Итого: Даже такая простая модель с таким малым количеством эпох обучения может иногда сгенерировать нечто осмысленное. Однако для генерации нормального текста необходимо длительное обучение и более сложная модель, из нескольких слоёв LSTM и Dropout после них, что, однако, потребовало бы вычислительные мощности, которых у меня нет в наличии. Иначе следует взять очень маленький текст.

6838
alexandrov_dmitrii_lab_7/rus_data.txt Normal file
View File

File diff suppressed because it is too large Load Diff

53
almukhammetov_bulat_lab_1/README.md Normal file
View File

@ -0,0 +1,53 @@
Вариант 2
Задание:
Используя код из пункта «Регуляризация и сеть прямого распространения «из [1] (стр. 228), сгенерируйте определенный тип данных и сравните на нем 3 модели (по варианту)Постройте графики, отобразите качество моделей, объясните полученные результаты.
Данные:
make_circles (noise=0.2, factor=0.5, random_state=rs) Модели: · Линейную регрессию · Полиномиальную регрессию (со степенью 3) · Гребневую полиномиальную регрессию (со степенью 3, alpha= 1.0)
Запуск:
Запустите файл lab1.py
Описание программы:
1. Генерирует набор данных с использованием функции make_circles из scikit-learn. Этот набор данных представляет собой два класса, где точки одного класса окружают точки другого класса с добавленным шумом.
2. Разделяет данные на обучающий и тестовый наборы с помощью функции train_test_split.
3. Создает три разные модели для классификации данных:
4. Линейная регрессия (Logistic Regression).
5. Полиномиальная регрессия третьей степени (Polynomial Regression).
6. Гребневая полиномиальная регрессия третьей степени с регуляризацией и альфой равной единице (Ridge Polynomial Regression).
7. Обучаем каждую из этих моделей на обучающем наборе данных и оцениваем их точность на тестовом наборе данных.
8. Выводит результаты точности каждой модели.
9. Разделение областей предсказаний моделей (границы решения).
10. Тестовые и обучающие точки, окрашенные в соответствии с классами. (красным и синим)
Результаты:
<p>
<div>Точность</div>
<img src="Рисунок1.png">
</p>
<p>
<div>Графики регрессии</div>
<img src="Рисунок2.png">
<img src="Рисунок3.png">
<img src="Рисунок4.png">
</p>
Исходя из получивших графиков и точночсти с данным типом генерации данных из этих трех моделей наиболее точной получились полиномиальную регрессия (со степенью 3) и гребневaz полиномиальная регрессия (со степенью 3, alpha= 1.0). Они так же являются идентичными между собой. Чтобы проверить это утверждение я провел дополнительное тестирование и написал скрипт, который для 10 разных random_state (2-11) вычисляет точность для трех разных моделей.
Результаты:
Значения точности для каждой модели:
Линейная регрессия 0.40 0.52 0.44 0.56 0.48 0.49 0.50 0.49 0.46 0.40
Полиномиальная регрессия (со степенью 3) 0.63 0.67 0.74 0.64 0.80 0.73 0.64 0.81 0.46 0.62
Гребневая полиномиальная регрессия (со степенью 3, alpha = 1.0) 0.63 0.67 0.74 0.64 0.80 0.73 0.64 0.81 0.46 0.62
Средние значения точности:
Линейная регрессия - Средняя точность: 0.47
Полиномиальная регрессия (со степенью 3) - Средняя точность: 0.68
Гребневая полиномиальная регрессия (со степенью 3, alpha = 1.0) - Средняя точность: 0.68
Утверждение также подтвердилось.

83
almukhammetov_bulat_lab_1/lab1.py Normal file
View File

@ -0,0 +1,83 @@
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.datasets import make_circles
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import StandardScaler
# Используя код из пункта «Регуляризация и сеть прямого распространения»из [1](стр. 228),
# сгенерируйте определенный тип данных и сравните на нем 3 модели (по варианту).
# Постройте графики, отобразите качество моделей, объясните полученные результаты.
# Модели
# Линейная регрессия
# Полиномиальная регрессия (со степенью 3)
# Гребневую полиномиальную регрессию (со степенью 3, alpha = 1.0)
# Данные
# make_circles (noise=0.2, factor=0.5, random_state=rs)
random_state = np.random.RandomState(2)
# Генерируем датасет
circles_dataset = make_circles(noise=0.2, factor=0.5, random_state=random_state)
X, y = circles_dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.9, random_state=random_state)
# Создаем модели
models = []
# Линейная регрессия
linear_model = LogisticRegression(random_state=random_state)
models.append(("Линейная регрессия", linear_model))
# Полиномиальная регрессия (со степенью 3)
poly_model = make_pipeline(PolynomialFeatures(degree=3), StandardScaler(),
LogisticRegression(random_state=random_state))
models.append(("Полиномиальная регрессия (со степенью 3)", poly_model))
# Гребневая полиномиальная регрессия (со степенью 3 и alpha=1.0)
ridge_poly_model = make_pipeline(PolynomialFeatures(degree=3), StandardScaler(),
LogisticRegression(penalty='l2', C=1.0, random_state=random_state))
models.append(("Гребневая полиномиальная регрессия (со степенью 3, alpha = 1.0)", ridge_poly_model))
# Обучаем и оцениваем модели
results = []
for name, model in models:
model.fit(X_train, y_train) # обучаем
y_pred = model.predict(X_test) # предсказываем
accuracy = accuracy_score(y_test, y_pred) # определяем точность
results.append((name, accuracy))
# Выводим результаты
for name, accuracy in results:
print(f"{name} - Точность: {accuracy:.2f}")
# Строим графики
cmap_background = ListedColormap(['#FFAAAA', '#AAAAFF'])
cmap_points = ListedColormap(['#FF0000', '#0000FF'])
plt.figure(figsize=(15, 5))
for i, (name, model) in enumerate(models):
plt.subplot(1, 3, i + 1)
xx, yy = np.meshgrid(np.linspace(X[:, 0].min() - 1, X[:, 0].max() + 1, 100),
np.linspace(X[:, 1].min() - 1, X[:, 1].max() + 1, 100))
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap=cmap_background, alpha=0.5)
plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cmap_points, marker='o', label='Тестовые точки')
plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cmap_points, marker='x', label='Обучающие точки')
plt.legend()
plt.title(name)
plt.text(0.5, -1.2, 'Красный класс', color='r', fontsize=12)
plt.text(0.5, -1.7, 'Синий класс', color='b', fontsize=12)
plt.tight_layout()
plt.show()

BIN
almukhammetov_bulat_lab_1/Рисунок1.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 33 KiB

BIN
almukhammetov_bulat_lab_1/Рисунок2.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 66 KiB

BIN
almukhammetov_bulat_lab_1/Рисунок3.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 46 KiB

BIN
almukhammetov_bulat_lab_1/Рисунок4.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 81 KiB

40
almukhammetov_bulat_lab_2/README.md Normal file
View File

@ -0,0 +1,40 @@
Вариант 2
Задание:
Используя код из [1](пункт «Решение задачи ранжирования признаков», стр. 205), выполните ранжирование признаков с помощью указанных по варианту моделей. Отобразите получившиеся значения\оценки каждого признака каждым методом\моделью и среднюю оценку. Проведите анализ получившихся результатов. Какие четыре признака оказались самыми важными по среднему значению? (Названия\индексы признаков и будут ответом на задание).
Данные:
Линейная регрессия (LinearRegression)
Рекурсивное сокращение признаков (Recursive Feature Elimination –RFE)
Сокращение признаков Случайными деревьями (Random Forest Regressor)
Запуск:
Запустите файл lab2.py
Описание программы:
1. Генерирует случайные данные для задачи регрессии с помощью функции make_regression, создавая матрицу признаков X и вектор целевой переменной y.
2. Создает DataFrame data, в котором столбцы представляют признаки, а последний столбец - целевую переменную.
3. Разделяет данные на матрицу признаков X и вектор целевой переменной y.
4. Создает список моделей для ранжирования признаков: линейной регрессии, рекурсивного сокращения признаков и сокращения признаков случайными деревьями.
5. Создает словарь model_scores для хранения оценок каждой модели.
6. Обучает и оценивает каждую модель на данных:
7. Вычисляет ранги признаков и нормализует их в диапазоне от 0 до 1.
8. Выводит оценки признаков каждой модели и их средние оценки.
9. Находит четыре наиболее важных признака по средней оценке и выводит их индексы и значения.
Результаты:
![Alt text](image.png)
![Alt text](image-1.png)
![Alt text](image-2.png)
![Alt text](image-3.png)
![Alt text](image-4.png)
Выводы:
Четыре наиболее важных признака, определенных на основе средних оценок, включают Признак 6, Признак 1, Признак 2 и Признак 5. Эти признаки имеют наибольшую среднюю важность среди всех признаков.

BIN
almukhammetov_bulat_lab_2/image-1.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 21 KiB

BIN
almukhammetov_bulat_lab_2/image-2.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 22 KiB

BIN
almukhammetov_bulat_lab_2/image-3.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 9.8 KiB

BIN
almukhammetov_bulat_lab_2/image-4.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 6.8 KiB

BIN
almukhammetov_bulat_lab_2/image.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 9.7 KiB

75
almukhammetov_bulat_lab_2/lab2.py Normal file
View File

@ -0,0 +1,75 @@
import numpy as np
import pandas as pd
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
from sklearn.feature_selection import RFE
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import MinMaxScaler
# Используя код из [1](пункт «Решение задачи ранжирования признаков», стр. 205), выполните ранжирование признаков
# с помощью указанных по варианту моделей. Отобразите получившиеся значения\оценки каждого признака каждым
# методом\моделью и среднюю оценку. Проведите анализ получившихся результатов. Какие четыре признака оказались
# самыми важными по среднему значению? (Названия\индексы признаков и будут ответом на задание).
# Линейная регрессия (LinearRegression), Рекурсивное сокращение признаков (Recursive Feature Elimination –RFE),
# Сокращение признаков Случайными деревьями (Random Forest Regressor)
random_state = np.random.RandomState(2)
# Генерация случайных данных для регрессии
X, y = make_regression(n_samples=100, n_features=10, noise=0.1, random_state=random_state)
# Создание DataFrame для данных
data = pd.DataFrame(X, columns=[f'признак_{i}' for i in range(X.shape[1])])
data['целевая_переменная'] = y
# Разделение данных на признаки (X) и целевую переменную (y)
X = data.drop('целевая_переменная', axis=1)
y = data['целевая_переменная']
# Создаем модели
models = [
("Линейная регрессия", LinearRegression()),
("Рекурсивное сокращение признаков", RFE(LinearRegression(), n_features_to_select=1)),
("Сокращение признаков Случайными деревьями", RandomForestRegressor())
]
# Словарь для хранения оценок каждой модели
model_scores = {}
# Обучение и оценка моделей
for name, model in models:
model.fit(X, y)
if name == "Рекурсивное сокращение признаков":
# RFE возвращает ранжирование признаков
rankings = model.ranking_
# Нормализация рангов так, чтобы они находились в диапазоне от 0 до 1
normalized_rankings = 1 - (rankings - 1) / (np.max(rankings) - 1)
model_scores[name] = normalized_rankings
elif name == "Сокращение признаков Случайными деревьями":
# Важность признаков для RandomForestRegressor
feature_importances = model.feature_importances_
# Нормализация значений важности признаков в диапазоне от 0 до 1
normalized_importances = MinMaxScaler().fit_transform(feature_importances.reshape(-1, 1))
model_scores[name] = normalized_importances.flatten()
elif name == "Линейная регрессия":
# Коэффициенты признаков для Linear Regression
coefficients = model.coef_
# Нормализация коэффициентов так, чтобы они находились в диапазоне от 0 до 1
normalized_coefficients = MinMaxScaler().fit_transform(np.abs(coefficients).reshape(-1, 1))
model_scores[name] = normalized_coefficients.flatten()
# Вывод оценок каждой модели
for name, scores in model_scores.items():
print(f"{name} оценки признаков:")
for feature, score in enumerate(scores, start=1):
print(f"Признак {feature}: {score:.2f}")
print(f"Средняя оценка: {np.mean(scores):.2f}")
print()
# Находим четыре наиболее важных признака по средней оценке
all_feature_scores = np.mean(list(model_scores.values()), axis=0)
sorted_features = sorted(enumerate(all_feature_scores, start=1), key=lambda x: x[1], reverse=True)
top_features = sorted_features[:4]
print("Четыре наиболее важных признака:")
for feature, score in top_features:
print(f"Признак {feature}: {score:.2f}")

84
antonov_dmitry_lab_2/README.md Normal file
View File

@ -0,0 +1,84 @@
# Лаб 2
Ранжирование признаков
Выполните ранжирование признаков с помощью указанных по варианту моделей.
Отобразите получившиеся значения\оценки каждого признака каждым методом\моделью и среднюю оценку.
Проведите анализ получившихся результатов.
Какие четыре признака оказались самыми важными по среднему значению?
(Названия\индексы признаков и будут ответом на задание).
# Вариант 3
Линейная регрессия (LinearRegression) , Сокращение признаков
Случайными деревьями (Random Forest Regressor), Линейная корреляция
(f_regression)
Я использовал датасет Predict students' dropout and academic success
https://www.kaggle.com/datasets/thedevastator/higher-education-predictors-of-student-retention
Он используется мной по заданию на курсовую работу
# Запуск
Выполнением скрипта файла (вывод в консоль).
# Модели:
1. Линейная регрессия (LinearRegression)
1. Сокращение признаков cлучайными деревьями (Random Forest Regressor)
1. Линейная корреляция (f_regression)
# Пояснения
<div>
Выбор наиболее подходящего метода ранжирования объектов зависит от специфики набора данных и требований
к модели.
Линейная регрессия - это простой и понятный метод, который может быть использован для предсказания значений.
Он хорошо работает, если зависимость между переменными является линейной.
Однако, если данные содержат сложные нелинейные зависимости, линейная регрессия может
оказаться не очень эффективной.
Уменьшение признаков с помощью случайных деревьев (Random Forest Regressor) - это мощный метод,
который способен обрабатывать сложные взаимосвязи в данных, даже если они нелинейные.
Он основан на идее создания ансамбля деревьев решений, каждое из которых дает свой голос за
наиболее подходящий ответ. Случайные леса обычно дают хорошие результаты и являются устойчивыми
к переобучению.
Линейная корреляция или f_regression - это статистический метод, который используется для измерения
степени связи между двумя переменными. Он может помочь определить, есть ли вообще связь между переменными,
но не подходит для ранжирования объектов.
</div>
### 4 самых важных признака в среднем:
1. Признак: Curricular units 2nd sem (approved), Оценка: 0.8428
2. Признак: Tuition fees up to date, Оценка: 0.4797
3. Признак: Curricular units 1st sem (approved), Оценка: 0.2986
4. Признак: Curricular units 2nd sem (grade), Оценка: 0.2778
### 4 самых важных для lr_scores линейной регрессии:
1. 0.3917 'Tuition fees up to date'
2. 0.2791 'International'
3. 0.2075 'Curricular units 2nd sem (approved)'
4. 0.1481 'Debtor'
### 4 самых важных для rf_scores рандом forests:
1. 0.4928 'Curricular units 2nd sem (approved)'
2. 0.061 'Tuition fees up to date'
3. 0.0458 'Curricular units 2nd sem (grade)'
4. 0.0308 'Curricular units 1st sem (grade)'
### 4 самых важных для f_regression:
1. 2822.104 'Curricular units 2nd sem (approved)'
2. 2093.3315 'Curricular units 2nd sem (grade)'
3. 1719.4229 'Curricular units 1st sem (approved)'
4. 1361.6144 'Curricular units 1st sem (grade)'
### Объяснение:
<div>
В общем, выбор между линейной регрессией и случайными лесами зависит от характеристик данных.
Если данные имеют линейную зависимость, то линейная регрессия будет предпочтительнее.
Если данные содержат сложные, возможно нелинейные взаимосвязи, то Random Forest может быть лучшим выбором.
В любом случае, важно провести предварительное исследование данных и тестирование различных моделей,
чтобы выбрать наиболее подходящую.
</div>

4425
antonov_dmitry_lab_2/dataset.csv Normal file
View File

File diff suppressed because it is too large Load Diff

106
antonov_dmitry_lab_2/lab2.py Normal file
View File

@ -0,0 +1,106 @@
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.feature_selection import f_regression
from sklearn.preprocessing import MinMaxScaler
# загрузка dataset
data = pd.read_csv('dataset.csv')
# разделение dataset на тренировочную и тестовую выборки
X = data.drop(['Target'], axis=1)
y = data['Target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Тренировка моделей
# Линейная регрессия
lr = LinearRegression()
lr.fit(X_train, y_train)
# Сокращение признаков случайными деревьями с помощью Random Forest Regressor
rf = RandomForestRegressor()
rf.fit(X_train, y_train)
# Ранжирование признаков использую каждую модель/метод
# Получение абсолютных значений коэффициентов в качестве оценок важности признаков
lr_scores = abs(lr.coef_)
# Получение оценок важности объектов из модели Random Forest Regressor
rf_scores = rf.feature_importances_
# Отображение итоговых оценок по каждой колонке
feature_names = X.columns.tolist()
# показать оценки рангов по модели линейной регрессии
print("оценки линейной регрессии:")
for feature, score in zip(feature_names, lr_scores):
print(f"{feature}: {round(score, 4)}")
# оценки метода рандомных лесов
print("\nоценки Random Forest:")
for feature, score in zip(feature_names, rf_scores):
print(f"{feature}: {round(score, 4)}")
# вычисление значений оценки для f_regression
f_scores, p_values = f_regression(X, y)
# оценки f_regression
print("\nоценки f_regression:")
for feature, score in zip(feature_names, f_scores):
print(f"{feature}: {round(score, 4)}")
# использую MinMaxScaler для точных средних значений рангов
scaler = MinMaxScaler()
lr_scores_scaled = scaler.fit_transform(lr_scores.reshape(-1, 1)).flatten()
rf_scores_scaled = scaler.fit_transform(rf_scores.reshape(-1, 1)).flatten()
f_scores_scaled = scaler.fit_transform(f_scores.reshape(-1, 1)).flatten()
# вычисление средних оценок для каждого признака
average_scores = {}
for feature in feature_names:
average_scores[feature] = (lr_scores_scaled[feature_names.index(feature)] +
rf_scores_scaled[feature_names.index(feature)] +
f_scores_scaled[feature_names.index(feature)]) / 3
# получаем среднюю оценку признаков
sorted_features = sorted(average_scores.items(), key=lambda x: x[1], reverse=True)
# получаем самых важных признака
top_4_features = sorted_features[:4]
# отображаем 4 самые важные
print("\n4 самых важных признака в среднем:")
for feature, score in top_4_features:
print(f"Признак: {feature}, Оценка: {round(score, 4)}")
# отображаем самых важных признака для каждого метода/модели
top_lr_indices = np.argsort(lr_scores)[-4:][::-1]
top_rf_indices = np.argsort(rf_scores)[-4:][::-1]
top_f_indices = np.argsort(f_scores)[-4:][::-1]
top_lr_features = [feature_names[i] for i in top_lr_indices]
top_rf_features = [feature_names[i] for i in top_rf_indices]
top_f_features = [feature_names[i] for i in top_f_indices]
top_lr_features_score = [lr_scores[i] for i in top_lr_indices]
top_rf_features_score = [rf_scores[i] for i in top_rf_indices]
top_f_features_score = [f_scores[i] for i in top_f_indices]
print("\n4 самых важных для lr_scores:")
print(top_lr_features)
for i in top_lr_features_score:
print(round(i, 4))
print("\n4 самых важных для rf_scores:")
print(top_rf_features)
for i in top_rf_features_score:
print(round(i, 4))
print("\n4 самых важных для f_scores:")
print(top_f_features)
for i in top_f_features_score:
print(round(i, 4))

85
antonov_dmitry_lab_3/README.md Normal file
View File

@ -0,0 +1,85 @@
# Лаб 3
Деревья решений
Часть 1. По данным о пассажирах Титаника решите задачу классификации
(с помощью дерева решений), в которой по различным характеристикам
пассажиров требуется найти у выживших пассажиров два наиболее важных
признака из трех рассматриваемых (по варианту). Пример решения задачи
можно посмотреть здесь: [1] (стр.188). Скачать данные можно по ссылке:
https://www.kaggle.com/datasets/heptapod/titanic
Часть 2. Решите с помощью библиотечной реализации дерева решений
задачу из лабораторной работы «Веб-сервис «Дерево решений» по предмету
«Методы искусственного интеллекта» на 99% ваших данных. Проверьте
работу модели на оставшемся проценте, сделайте вывод.
# Вариант 3
Признаки Sex,Age,SibSp
# Запуск
Выполнением скрипта файла (вывод в консоль).
# Описание модели:
DecisionTreeClassifier - это алгоритм машинного обучения, используемый для задач классификации и регрессии.
Он представляет собой дерево решений, где на каждом узле дерева решается, какой вопрос задать дальше
(признак для дальнейшего разбиения данных), а в листьях находятся окончательные ответы.
# Результаты
На данных для Титаника модель определяет важность признаков с точность 75% (исключает 'sibsp').
Эти два признака обладают статистической важностью.
<p>
<div>Титаник</div>
<img src="screens/titanic.png" width="650" title="Титаник 1">
</p>
На данных моего датасета модель справляется на 52.768%, если в качестве предлагаемых параметров
на вход идут ['Gender', 'Debtor', 'International'] (исключает 'International').
<p>
<div>Мой датасет 1</div>
<img src="screens/mydataset1.png" width="650" title="Мой датасет 1">
</p>
И на 70.961, если на вход идут ['Gender', 'Debtor', 'Curricular units 2nd sem (approved)']
(исключает 'Gender').
<p>
<div>Мой датасет 2</div>
<img src="screens/mydataset2.png" width="650" title="Мой датасет 2">
</p>
Такой результат можно объяснить большей значимостью признака 'Curricular units 2nd sem (approved)'
вместо 'International' (было показано в предыдущей лабораторной).
Из-за того, что мы взяли статистически более значимый признак, модель выдает нам большую точность.
Точность 52.768% указывает на то, что модель работает на уровне случайности, что означает, что она
работает не лучше, чем случайное угадывание. Для этого может быть несколько причин:
1. Признаки все имеет малое значение: то есть для сравнения подаются признаки статистически малозначимые.
2. Недостаточно данных: Набор данных может содержать недостаточно информации или примеров для
изучения моделью. Если набор данных невелик или нерепрезентативен, модель, возможно, не сможет
хорошо обобщить новые данные.
3. Несбалансированные классы: Если классы в вашей целевой переменной несбалансированы
(например, случаев, не связанных с отсевом, гораздо больше, чем случаев отсева), модель может
быть смещена в сторону прогнозирования класса большинства.
4. Переобучение: Модель может быть переобучена обучающими данным, что означает, что она изучает шум
в данных, а не лежащие в их основе закономерности. Это может произойти, если модель слишком сложна по
сравнению с объемом доступных данных.
5. Недостаточное соответствие: С другой стороны, модель может быть слишком простой, чтобы отразить
взаимосвязи в данных. Важно выбрать соответствующий уровень сложности модели.
<div>
При отборе признаков должна учитываться их статистическая значимость, вычисленная различными способами
(например с помощью лин регрессии, Random Forest Regressor, линейной корреляции f_regression или других).
Так же должно быть достаточно данных, в модели должно быть сведено к минимуму переобучение.
</div>

4425
antonov_dmitry_lab_3/dataset.csv Normal file
View File

File diff suppressed because it is too large Load Diff

35
antonov_dmitry_lab_3/lab3.py Normal file
View File

@ -0,0 +1,35 @@
import pandas as pd
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
# прочитали датасет
data = pd.read_csv('dataset.csv')
# определение признаков
# целевая переменная - Target
X = data[['Gender', 'Debtor', 'Curricular units 2nd sem (approved)']]
y = data['Target'] # Assuming 'Dropout' is the target variable
# разделили данные на тренировочную и тестовую выборки
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# создали модель decision tree classifier
dt_classifier = DecisionTreeClassifier(random_state=42)
dt_classifier.fit(X_train, y_train)
# получили значения модели для 2ух самых важных признаков
feature_importances = dt_classifier.feature_importances_
top_features_indices = feature_importances.argsort()[-2:][::-1]
top_features = X.columns[top_features_indices]
# вывод результата
print("2 самых важных признака:", top_features)
# получили значения модели для проверки точности
predictions = dt_classifier.predict(X_test)
# вычислили точность модели
accuracy = accuracy_score(y_test, predictions)
print("точность модели:", accuracy)

BIN
antonov_dmitry_lab_3/screens/mydataset1.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 14 KiB

BIN
antonov_dmitry_lab_3/screens/mydataset2.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 16 KiB

BIN
antonov_dmitry_lab_3/screens/titanic.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 11 KiB

40
antonov_dmitry_lab_3/titanic.py Normal file
View File

@ -0,0 +1,40 @@
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
# прочитали датасет
data = pd.read_csv("titanic_data.csv")
# определение признаков
features = ['Sex', 'Age', 'sibsp']
# целевая переменная - выжившие
target = 'Survived'
# разделили данные на тренировочную и тестовую выборки
train_data, test_data, train_labels, test_labels = train_test_split(
data[features],
data[target],
test_size=0.2,
random_state=42
)
# создали модель decision tree classifier
model = DecisionTreeClassifier()
# натренировали модель
model.fit(train_data, train_labels)
# получили значения модели для проверки точности
predictions = model.predict(test_data)
# вычислили точность модели
accuracy = accuracy_score(test_labels, predictions)
print("точность модели:", accuracy)
# нашли два самых важных признака
importances = model.feature_importances_
indices = (-importances).argsort()[:2]
important_features = [features[i] for i in indices]
print("два самых важных признака:", important_features)

1310
antonov_dmitry_lab_3/titanic_data.csv Normal file
View File

File diff suppressed because it is too large Load Diff

78
antonov_dmitry_lab_4/README.md Normal file
View File

@ -0,0 +1,78 @@
# Лаб 4 Кластеризация
Использовать метод кластеризации по варианту для данных из датасета курсовой
Predict students' dropout and academic success (отсев студентов), самостоятельно сформулировав задачу.
Интерпретировать результаты и оценить, насколько хорошо он подходит для
решения сформулированной вами задачи.
# Вариант 3
Метод t-SNE
# Запуск
Выполнением скрипта файла (вывод в консоль).
# Описание модели:
T-Distributed Stochastic Neighbor Embedding (t-SNE) - это метод визуализации и снижения размерности,
используемый для визуализации многомерных данных в виде двумерной или трехмерной графики.
Результатом работы t-SNE является визуализация данных, где близкие точки в исходном пространстве отображаются
близко друг к другу, а отдаленные точки - далеко. Это позволяет исследователям изучать структуру данных и
находить кластеры и структуры, которые могут быть не видны при прямом наблюдении исходного пространства высокой размерности.
# Задача кластеризации
Учитывая набор данных, содержащий информацию о студентах, включая их пол, международный статус и ВВП,
цель состоит в том, чтобы сгруппировать этих студентов в отдельные кластеры на основе этих признаков.
Цель состоит в том, чтобы выявить естественные закономерности или подгруппы среди учащихся, которые могут
иметь сходные характеристики с точки зрения пола, международного статуса и экономического происхождения.
Такая кластеризация может помочь в адаптации образовательных программ, служб поддержки или вмешательств
к конкретным группам учащихся для улучшения академических результатов и показателей удержания.
Цель анализа - выявить значимые идеи, которые могут быть использованы для улучшения общего образовательного опыта
и показателей успешности различных групп учащихся.
# Результаты
Для применения метода уменьшения размерности t-SNE использованы признаки "Гендер", "Международный" и "ВВП".
Данные проецируются на двумерную плоскость, при этом сохраняя локальную структуру данных.
Как интерпретировать результаты на графике:
1. Пол:
- Поскольку "Пол" является категориальной переменной (бинарной, как "Мужчина" или "Женщина"),
- Ожидается увидеть на графике отчетливые кластеры или разделения. Каждая точка представляет учащегося,
- и лица одинакового пола должны быть сгруппированы вместе.
2. Международный:
- "Международный" также является бинарной категориальной переменной (например, "Да" или "Нет" указывает,
- является ли студент иностранным), вы можете увидеть разделение между иностранными и немеждународными студентами.
- Это может привести к образованию двух различных кластеров.
3. ВВП:
- "ВВП" - это непрерывная переменная, и ее значения будут представлены в виде точек на графике. В зависимости от
- распределения значений ВВП вы можете наблюдать градиент или закономерность в данных.
Теперь, когда посмотреть на график, должны быть видны точки, разбросанные по двумерному пространству. Похожие точки
находятся близко друг к другу, а непохожие - дальше друг от друга.
- Результаты:
- Видны четкие кластеры, это говорит о том, что эти признаки являются хорошими показателями для разделения
- студентов на группы.
- Доминирующими признаками являются "гендер" и "Интернациональность", можно увидеть два различных кластера,
- в одном из которых, например, в основном учатся местные студенты мужского пола, а в другом - иностранные студентки
- женского пола.
- "ВВП" оказывает сильное влияние, можно увидеть градиент точек, указывающий на корреляцию между ВВП и
- некоторой базовой закономерностью в данных.
Конкретная интерпретация будет зависеть от фактического распределения и характеристик данных.
Также важно отметить, что t-SNE - это стохастический алгоритм, поэтому его многократное выполнение с одними и теми
же параметрами может привести к несколько иным результатам. Поэтому рекомендуется изучить графики из нескольких прогонов,
чтобы получить четкое представление о структуре данных.
<p>
<div>График</div>
<img src="screens/myplot.png" width="650" title="График">
</p>

4425
antonov_dmitry_lab_4/dataset.csv Normal file
View File

File diff suppressed because it is too large Load Diff

22
antonov_dmitry_lab_4/lab4.py Normal file
View File

@ -0,0 +1,22 @@
import pandas as pd
from sklearn.cluster import KMeans
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
# загрузка датасета
data = pd.read_csv('dataset.csv')
# выделение необходимых признаков
X = data[['Gender', 'International', 'GDP']]
# применение t-SNE для сокращения размерности
tsne = TSNE(n_components=2, random_state=42)
X_tsne = tsne.fit_transform(X)
# визуализация данных
plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=data['Target'], cmap='viridis')
plt.colorbar()
plt.xlabel('t-SNE х')
plt.ylabel('t-SNE у')
plt.title('t-SNE визуализация')
plt.show()

BIN
antonov_dmitry_lab_4/screens/myplot.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 37 KiB

42
antonov_dmitry_lab_5/README.md Normal file
View File

@ -0,0 +1,42 @@
# Лаб 5 Регрессия
Использовать регрессию по варианту для данных из датасета курсовой
Predict students' dropout and academic success (отсев студентов),
самостоятельно сформулировав задачу. Оценить, насколько хорошо она подходит
для решения сформулированной вами задачи.
# Вариант 3
Лассо-регрессия
# Запуск
Выполнением скрипта файла (вывод в консоль).
# Описание модели:
Лассо (Lasso) — это метод регрессионного анализа, который используется в статистике и
машинном обучении для предсказания значения зависимой переменной.
Регрессия Лассо использует регуляризацию L1 для добавления штрафа, равного абсолютному
значению коэффициентов. Это уменьшает некоторые коэффициенты и устанавливает другие равными 0,
выполняя автоматический выбор функции. Обычная регрессия не имеет регуляризации.
# Задача регрессии
Для прогнозирования отсева учащихся и набора данных об успеваемости спрогнозируйте отсев
используя регрессию Лассо для признаков
'Curricular units 2nd sem (approved)' - (Учебные блоки 2-го семестра (утверждены))
'Curricular units 2nd sem (grade)' - (Учебные блоки 2-го семестра (класс))
'Tuition fees up to date' - (Стоимость обучения")
# Результаты
Точность регрессии для вышеперечисленных признаков составили 0.6256 (alpha = 0.01)
При изменении коэффициента регуляризации в диапозоне от 0.01 до 1.5 наблюдается только ухудшение качества
модели, таким образом для заданных параметров подходит больше обычная модель линейной регрессии, так как
по этим признакам судя по результатам наблюдается линейная зависимость.
Для этих признаков модель регрессии подходит плохо, нужно искать другую.
<p>
<div>График</div>
<img src="screens/myplot.png" width="650" title="График">
</p>

4425
antonov_dmitry_lab_5/dataset.csv Normal file
View File

File diff suppressed because it is too large Load Diff

47
antonov_dmitry_lab_5/lab5.py Normal file
View File

@ -0,0 +1,47 @@
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import Lasso
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
# загрузка данных
data = pd.read_csv('dataset.csv')
X = (data[
['Curricular units 2nd sem (approved)',
'Tuition fees up to date',
'Curricular units 2nd sem (grade)']]
)
y = data['Target']
# тренировка модели
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
lasso_model = Lasso(alpha=0.01)
lasso_model.fit(X_train, y_train)
# оценка модели
y_pred_train = lasso_model.predict(X_train)
y_pred_test = lasso_model.predict(X_test)
# оценка результатов модели
train_accuracy = accuracy_score(y_train, np.round(y_pred_train))
test_accuracy = accuracy_score(y_test, np.round(y_pred_test))
# вывод результатов
print(f"Тренировочная Accuracy: {train_accuracy}")
print(f"Тест Accuracy: {test_accuracy}")
# коэффициенты значимости признаков
coefficients = lasso_model.coef_
feature_names = X.columns
# вывод в консоль коэффициентов значимости
for feature, coef in zip(feature_names, coefficients):
print(f"{feature}: {coef}")
plt.figure(figsize=(10, 6))
plt.barh(feature_names, coefficients)
plt.xlabel('коэффициент')
plt.title('Значимости признаков по регрессии Лассо')
plt.show()

BIN
antonov_dmitry_lab_5/screens/myplot.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 19 KiB

89
antonov_dmitry_lab_6/README.md Normal file
View File

@ -0,0 +1,89 @@
# Лаб 6 Нейронная сеть
Использовать нейронную сеть MLPClassifier по варианту для данных из датасета курсовой
Predict students' dropout and academic success (отсев студентов),
самостоятельно сформулировав задачу. Оценить, насколько хорошо она подходит
для решения сформулированной вами задачи.
# Вариант 3
Нейронная сеть MLPClassifier
# Запуск
Выполнением скрипта файла (вывод в консоль).
# Задача регрессии
Для прогнозирования отсева учащихся и набора данных об успеваемости спрогнозируйте отсев
используя нейронную сеть для признаков
'Curricular units 2nd sem (approved)' - (Учебные блоки 2-го семестра (утверждены))
'Curricular units 2nd sem (grade)' - (Учебные блоки 2-го семестра (класс))
'Tuition fees up to date' - (Стоимость обучения")
# Описание модели:
"MLPClassifier" - это тип искусственной нейронной сети прямого действия, которая широко используется для задач классификации.
Объяснение некоторых ключевых параметров:
1. hidden_layer_sizes:
- Этот параметр определяет количество нейронов в каждом скрытом слое и количество скрытых слоев в сети.
- Это кортеж, где каждый элемент представляет количество нейронов в определенном скрытом слое.
- Например, `hidden_layer_sizes=(100, 100)` означает, что есть два скрытых слоя, причем первый слой
- содержит 100 нейронов, а второй слой также содержит 100 нейронов.
2. activation:
- Этот параметр определяет функцию активации для скрытых слоев. Функция активации привносит
нелинейность в сеть, позволяя ей изучать сложные паттерны.
- Распространенные варианты включают:
- "identity": линейная функция активации (обычно не используется на практике).
- "logistic": сигмовидная логистическая функция
- "tanh": гиперболическая касательная функция
- "relu": Выпрямленная линейная единица измерения
3. solver:
- Этот параметр определяет алгоритм, используемый для оптимизации весов нейронной сети.
- Распространенные варианты включают:
- `adam": оптимизатор на основе стохастического градиента, сочетающий идеи RMSProp и Momentum.
- `sgd": Стохастический градиентный спуск.
- `lbfgs": алгоритм Бройдена-Флетчера-Гольдфарба-Шанно с ограниченной памятью.
4. alpha:
- Параметр штрафа L2 (условие регуляризации). Это помогает предотвратить переобучение,
наказывая за большие веса.
- Более высокие значения "альфа" приводят к более сильной регуляризации.
5. max_iter:
- Максимальное количество итераций для тренировочного процесса. Этот параметр помогает
предотвратить бесконечное обучение модели.
6. learning_rate:
- График скорости обучения для обновления веса. Он определяет размер шага, с которым веса
обновляются во время тренировки.
- Опции включают 'constant', 'invscaling', и 'adaptive'.
7. random_state:
- Начальное значение, используемое генератором случайных чисел. Установка начального значения
гарантирует воспроизводимость результатов.
8. batch_size:
- Количество образцов, использованных в каждой мини-партии во время обучения. Это влияет
на скорость конвергенции и использование памяти.
9. early_stopping:
- Если установлено значение "True", обучение прекратится, если оценка проверки не улучшится.
Это помогает предотвратить переобучение.
10. validation_fraction:
- Доля обучающих данных, которую следует отложить в качестве валидационного набора для ранней
остановки.
# Результат:
Из прошлой лабораторной точность регрессии для вышеперечисленных признаков составила 0.6256 (alpha = 0.01)
Точность нейронной сети для вышеперечисленных признаков составила 72.32%
(при изменении описанных выше параметров оценка не улучается)
На примере тех же самых признаков нейронная сеть обеспечивает
лучшее качество предсказания отсева студентов.
<p>
<div>Результат</div>
<img src="screens/img.png" width="650" title="Результат">
</p>

4425
antonov_dmitry_lab_6/dataset.csv Normal file
View File

File diff suppressed because it is too large Load Diff

51
antonov_dmitry_lab_6/lab6.py Normal file
View File

@ -0,0 +1,51 @@
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import StandardScaler
# загрузка датасета
data = pd.read_csv('dataset.csv')
# выбор признаков
features = [
'Curricular units 2nd sem (approved)',
'Curricular units 2nd sem (grade)',
'Tuition fees up to date',
]
target = 'Target'
X = data[features]
y = data[target]
# разбиваем на тестовую и тренировочную выборки
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# стандартизация признаков
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
# тренируем нейронную сеть MLPClassifier
classifier = MLPClassifier(
hidden_layer_sizes=(50, 50), # два скрытых слоя с 50 нейронами каждый
activation='relu', # relu функция активации
solver='adam', # оптимизатор на основе стохастического градиента
alpha=0.0001, # L2 штраф (регуляризация)
max_iter=1000, # макс итераций
learning_rate='constant', # постоянная скорость обучения
random_state=42, # Random начало для воспроизведения результата
batch_size=32, # размер мини партии
early_stopping=True, # для предотвращения переобучения
validation_fraction=0.2, # 20% данных для проверки
verbose=True, # для оттображения итераций
)
classifier.fit(X_train, y_train)
# предсказываем значение
y_pred = classifier.predict(X_test)
# оцениваем результат
accuracy = np.mean(y_pred == y_test)
print(f'Оценка точности: {accuracy*100:.2f}%')

BIN
antonov_dmitry_lab_6/screens/img.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 44 KiB

115
antonov_dmitry_lab_7/README.md Normal file
View File

@ -0,0 +1,115 @@
# Лаб 7 RNN
Выбрать художественный текст (четные варианты – русскоязычный,
нечетные – англоязычный) и обучить на нем рекуррентную нейронную сеть
для решения задачи генерации. Подобрать архитектуру и параметры так,
чтобы приблизиться к максимально осмысленному результату. Далее
разбиться на пары четный-нечетный вариант, обменяться разработанными
сетями и проверить, как архитектура товарища справляется с вашим текстом.
В завершении подобрать компромиссную архитектуру, справляющуюся
достаточно хорошо с обоими видами текстов.
# Вариант 3
Рекуррентная нейронная сеть и задача
генерации текста
# Запуск
Выполнением скрипта файла (вывод в консоль).
# Описание модели:
Использованы библиотеки:
* numpy (np): популярная библиотека для научных вычислений.
* tensorflow (tf): библиотека для тренировки нейросетей.
* Sequential: тип Keras модель которая позволяет создавать нейросети слой за слоем.
* Embedding, LSTM, Dense: различные типы слоев в нейросетях.
* Tokenizer: класс для конвертации слов в числовой понятный для нейросети формат.
<p></p>
Каждая строка текста переводится в числа с помощью Tokernizer.
Класс Tokenizer в Keras - это утилита обработки текста, которая преобразует текст в
последовательность целых чисел. Он присваивает уникальное целое число (индекс) каждому слову
в тексте и создает словарь, который сопоставляет каждое слово с соответствующим индексом.
Это позволяет вам работать с текстовыми данными в формате, который может быть передан в нейронную сеть.
Все это записывается в input_sequences.
Строим RNN модель используя Keras:
* Embedding: Этот слой превращает числа в векторы плотности фиксированного размера. Так же известного
как "word embeddings". Вложения слов - это плотные векторные представления слов в непрерывном
векторном пространстве.Они позволяют нейронной сети изучать и понимать взаимосвязи между словами
на основе их контекста в содержании текста.
* LSTM: это тип рекуррентной нейронной сети (RNN), которая предназначена для обработки
зависимостей в последовательностях.
* Dense: полносвязный слой с множеством нейронов, нейронов столько же сколько и уникальных слов.
Он выводит вероятность следующего слова.
* Модель обучаем на разном количестве эпох, по умолчанию epochs = 100 (итераций по всему набору данных).
Определеяем функцию generate_text которая принимает стартовое слово, а также, число слов для генерации.
Модель генерирует текст путем многократного предсказания следующего слова на основе предыдущих слов в
начальном тексте.
* В конце мы получаем сгенерированную на основе текста последовательность.
# Задача генерации англоязычного текста
На вход подаем историю с похожими повторяющимися слова. Историю сохраняем в файл.
Задача проверить насколько сеть не станет повторять текст, а будет действительно генерировать
относительно новый текст.
# Результаты
Тестируется английский текст, приложенный в репозитории.
* на 50 эпохах ответ на I want
* I want to soar high up in the sky like to glide through the clouds feeling the wind beneath my wings i want to fly i want to fly i want to fly i want to fly i want to fly i want to fly i want to fly i want to
* на 100 эпох ответ на I want
* I want to fly i want to soar high up in the sky like a bird to glide through the clouds feeling the wind beneath my wings i want to fly i want to fly i want to spread my wings and soar into the open sky to glide far above the
* на 150 эпохах ответ на I want
* I want to fly i want to spread my wings and soar into the open sky to glide far above the earth unbounded by gravity i want to fly i want to fly i want to fly i want to soar high up in the sky like a bird to glide through
* на 220 эпохах ответ на I want
* I want to fly i want to soar high up in the sky like a bird to glide through the clouds feeling the wind beneath my wings i want to fly i want to fly i want to fly i want to fly i want to fly i want to fly i
* На 220 эпохах результаты хуже, это произошло скорее всего из-за переобучения(грубый повтор).
* На 50 эпохах нейронная сеть плохо обучена (из 1 места плюс повтор)
* На 100 эпохах средний результат (из 2 мест)
* На 150 эпохах нейронная сеть показывает наилучший результат (из 3 разных мест без повтора)
Так же модель работает и на русском тексте. Вот что сгенерировала модель на 150 эпохах.
Предложения взяты из разных мест и выглядят осмысленно.
"Я хочу летать потому что в этом заложено желание преодолевать границы хочу чувствовать себя
свободным словно ветер несущим меня к новым приключениям я хочу летать и продолжать этот бескрайний
полет вперед ибо в этом полете заключена вся суть моего существования существования существования
существования существования трудности трудности трудности неважными хочу летать потому что."
Чем больше текст мы берем, тем более интересные результаты получаем, но моих вычислительных мощностей уже не хватит.
Так же чем больше прогонов, тем лучше модель, но тоже не до бесконечности можно получить хороший результат.
<p>
<div>Обучение</div>
<img src="screens/img_2.png" width="650" title="Обучение">
</p>
<p>
<div>Результат</div>
<img src="screens/img_3.png" width="650" title="Результат">
</p>
<p>
<div>Обучение 1</div>
<img src="screens/step1.png" width="650" title="Обучение 1">
</p>
<p>
<div>Обучение 2</div>
<img src="screens/step2.png" width="650" title="Обучение 2">
</p>
<p>
<div>Обучение 3</div>
<img src="screens/step3.png" width="650" title="Обучение 3">
</p>
<p>
<div>Обучение 4</div>
<img src="screens/step4.png" width="650" title="Обучение 4">
</p>
<p>
<div>Обучение 5</div>
<img src="screens/step5.png" width="650" title="Обучение 5">
</p>

55
antonov_dmitry_lab_7/lab7.py Normal file
View File

@ -0,0 +1,55 @@
import numpy as np
from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dense
from keras.preprocessing.text import Tokenizer
from keras_preprocessing.sequence import pad_sequences
# загрузка текста
with open('rus.txt', encoding='utf-8') as file:
text = file.read()
tokenizer = Tokenizer()
tokenizer.fit_on_texts([text])
total_words = len(tokenizer.word_index) + 1
input_sequences = []
for line in text.split('\n'):
token_list = tokenizer.texts_to_sequences([line])[0]
for i in range(1, len(token_list)):
n_gram_sequence = token_list[:i + 1]
input_sequences.append(n_gram_sequence)
max_sequence_length = max([len(x) for x in input_sequences])
input_sequences = pad_sequences(input_sequences, maxlen=max_sequence_length, padding='pre')
predictors, labels = input_sequences[:, :-1], input_sequences[:, -1]
# создание RNN модели
model = Sequential()
model.add(Embedding(total_words, 100, input_length=max_sequence_length - 1))
model.add(LSTM(150))
model.add(Dense(total_words, activation='softmax'))
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# тренировка модели
model.fit(predictors, labels, epochs=150, verbose=1)
# генерация текста на основе модели
def generate_text(seed_text, next_words, model, max_sequence_length):
for _ in range(next_words):
token_list = tokenizer.texts_to_sequences([seed_text])[0]
token_list = pad_sequences([token_list], maxlen=max_sequence_length - 1, padding='pre')
predicted = np.argmax(model.predict(token_list), axis=-1)
output_word = ""
for word, index in tokenizer.word_index.items():
if index == predicted:
output_word = word
break
seed_text += " " + output_word
return seed_text
generated_text = generate_text("Я хочу", 50, model, max_sequence_length)
print(generated_text)

BIN
antonov_dmitry_lab_7/my_model.h5 Normal file
View File

Binary file not shown.

11
antonov_dmitry_lab_7/rus.txt Normal file
View File

@ -0,0 +1,11 @@
Я хочу летать. Почувствовать ветер в лицо, свободно парить в небесах. Я хочу летать, словно птица, освободившись от земных оков. Летать, словно орел, покоряя небесные просторы. Я хочу летать, чувствовать каждый момент поднятия в воздух, каждый поворот, каждое крыло, взмахнувшее в танце с аэродинамикой.
Я хочу летать над горами, смотреть на вершины, которые кажутся такими далекими с земли. Хочу летать над океанами, наблюдая за волнами, встречая закаты, окрашивающие водную гладь в огонь. Я хочу летать над городами, где жизнь бурлит своим ритмом, а улицы выглядят как мозаика, расстилающаяся под ногами.
Я хочу летать, ощущать тот подъем, когда ты понимаешь, что земля осталась позади, а ты – свободен, как никогда. Я хочу летать и видеть этот мир с высоты, где все проблемы кажутся такими маленькими и неважными. Хочу летать и чувствовать себя частью этого огромного космического танца, где звезды танцуют свои вечерние вальсы.
Я хочу летать, несмотря ни на что, преодолевая любые преграды. Хочу летать, потому что в этом чувствую свое настоящее "я". Летать – значит освобождаться от гравитации рутины, подниматься над повседневностью, смотреть на мир с высоты своей мечты.
Я хочу летать, потому что в этом заключена свобода души. Хочу ощутить, как воздух обволакивает меня, как каждая клетка моего тела ощущает эту свободу. Хочу летать, потому что это моя мечта, которая дает мне силы двигаться вперед, преодолевая все трудности.
Я хочу летать, потому что в этом заложено желание преодолевать границы. Хочу чувствовать себя свободным, словно ветер, несущим меня к новым приключениям. Я хочу летать и продолжать этот бескрайний полет вперед, ибо в этом полете заключена вся суть моего существования.

BIN
antonov_dmitry_lab_7/screens/img.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 40 KiB

BIN
antonov_dmitry_lab_7/screens/img_1.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 32 KiB

BIN
antonov_dmitry_lab_7/screens/img_2.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 44 KiB

BIN
antonov_dmitry_lab_7/screens/img_3.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 13 KiB

BIN
antonov_dmitry_lab_7/screens/img_4.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 46 KiB

BIN
antonov_dmitry_lab_7/screens/step1.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 76 KiB

BIN
antonov_dmitry_lab_7/screens/step2.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 81 KiB

BIN
antonov_dmitry_lab_7/screens/step3.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 72 KiB

BIN
antonov_dmitry_lab_7/screens/step4.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 63 KiB

BIN
antonov_dmitry_lab_7/screens/step5.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 29 KiB

Some files were not shown because too many files have changed in this diff Show More