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## 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)

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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()

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## 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)

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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)

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abanin_daniil_lab_2/lab2.py Normal file
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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()

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abanin_daniil_lab_3/README.md Normal file
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## 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
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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
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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
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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

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## 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

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abanin_daniil_lab_4/lab4.py Normal file
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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()

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abanin_daniil_lab_4/loan.csv Normal file
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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
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## 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

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abanin_daniil_lab_5/lab5.py Normal file
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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()

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abanin_daniil_lab_5/loan.csv Normal file
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@ -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
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alexandrov_dmitrii_lab_2/lab2.py Normal file
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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
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### 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.

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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()

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### 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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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252,0,3,"Strom, Mrs. Wilhelm (Elna Matilda Persson)",female,29,1,1,347054,10.4625,G6,S
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256,1,3,"Touma, Mrs. Darwis (Hanne Youssef Razi)",female,29,0,2,2650,15.2458,,C
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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
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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
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271,0,1,"Cairns, Mr. Alexander",male,,0,0,113798,31,,S
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273,1,2,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41,0,1,250644,19.5,,S
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276,1,1,"Andrews, Miss. Kornelia Theodosia",female,63,1,0,13502,77.9583,D7,S
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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
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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
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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
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303,0,3,"Johnson, Mr. William Cahoone Jr",male,19,0,0,LINE,0,,S
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315,0,2,"Hart, Mr. Benjamin",male,43,1,1,F.C.C. 13529,26.25,,S
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317,1,2,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24,1,0,244367,26,,S
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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
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332,0,1,"Partner, Mr. Austen",male,45.5,0,0,113043,28.5,C124,S
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337,0,1,"Pears, Mr. Thomas Clinton",male,29,1,0,113776,66.6,C2,S
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344,0,2,"Sedgwick, Mr. Charles Frederick Waddington",male,25,0,0,244361,13,,S
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348,1,3,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S
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352,0,1,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35,C128,S
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356,0,3,"Vanden Steen, Mr. Leo Peter",male,28,0,0,345783,9.5,,S
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360,1,3,"Mockler, Miss. Helen Mary ""Ellie""",female,,0,0,330980,7.8792,,Q
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364,0,3,"Asim, Mr. Adola",male,35,0,0,SOTON/O.Q. 3101310,7.05,,S
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366,0,3,"Adahl, Mr. Mauritz Nils Martin",male,30,0,0,C 7076,7.25,,S
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376,1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C
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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
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390,1,2,"Lehmann, Miss. Bertha",female,17,0,0,SC 1748,12,,C
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393,0,3,"Gustafsson, Mr. Johan Birger",male,28,2,0,3101277,7.925,,S
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397,0,3,"Olsson, Miss. Elina",female,31,0,0,350407,7.8542,,S
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411,0,3,"Sdycoff, Mr. Todor",male,,0,0,349222,7.8958,,S
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415,1,3,"Sundman, Mr. Johan Julian",male,44,0,0,STON/O 2. 3101269,7.925,,S
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417,1,2,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34,1,1,28220,32.5,,S
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419,0,2,"Matthews, Mr. William John",male,30,0,0,28228,13,,S
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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
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424,0,3,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28,1,1,347080,14.4,,S
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446,1,1,"Dodge, Master. Washington",male,4,0,2,33638,81.8583,A34,S
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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
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615,0,3,"Brocklebank, Mr. William Alfred",male,35,0,0,364512,8.05,,S
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621,0,3,"Yasbeck, Mr. Antoni",male,27,1,0,2659,14.4542,,C
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682,1,1,"Hassab, Mr. Hammad",male,27,0,0,PC 17572,76.7292,D49,C
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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
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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
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155 154 0 3 van Billiard, Mr. Austin Blyler male 40.5 0 2 A/5. 851 14.5 S
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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()

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### 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.

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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()

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### 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.

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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()

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### 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.

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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()

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### 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.

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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.

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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()

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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.

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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}")

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# 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>

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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))

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# 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>

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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)

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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)

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# 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>

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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()

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# 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>

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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()

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# 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>

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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}%')

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# 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>

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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)

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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.

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