podkorytova_yulia_lab_6

Reviewed-on: http://student.git.athene.tech/Alexey/IIS_2023_1/pulls/45

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abanin_daniil_lab_1/README.md

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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)

abanin_daniil_lab_1/lab1.py

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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()

abanin_daniil_lab_2/README.md

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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)

abanin_daniil_lab_2/RadomizedLasso.py

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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)

abanin_daniil_lab_2/lab2.py

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

abanin_daniil_lab_3/README.md

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

abanin_daniil_lab_3/lab3.py

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

abanin_daniil_lab_3/loan.csv

@@ -0,0 +1,615 @@
+Loan_ID,Gender,Married,Dependents,Education,Self_Employed,ApplicantIncome,CoapplicantIncome,LoanAmount,Loan_Amount_Term,Credit_History,Property_Area,Loan_Status
+LP001002,Male,No,0,1,No,5849,0.0,360.0,1.0,0,Y,0.0
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+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
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+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
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+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
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+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
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+LP001280,Male,Yes,2,0,No,3333,2000.0,99.0,360.0,0,Semiurban,1.0
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+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
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+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

abanin_daniil_lab_4/README.md

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

abanin_daniil_lab_4/lab4.py

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

abanin_daniil_lab_4/loan.csv

@@ -0,0 +1,615 @@
+Loan_ID,Gender,Married,Dependents,Education,Self_Employed,ApplicantIncome,CoapplicantIncome,LoanAmount,Loan_Amount_Term,Credit_History,Property_Area,Loan_Status
+LP001002,Male,No,0,1,0.0,5849,0.0,360.0,1.0,0,Y,0.0
+LP001003,Male,Yes,1,1,0.0,4583,1508.0,128.0,360.0,1,Rural,0.0
+LP001005,Male,Yes,0,1,1.0,3000,0.0,66.0,360.0,1,Urban,1.0
+LP001006,Male,Yes,0,0,0.0,2583,2358.0,120.0,360.0,1,Urban,1.0
+LP001008,Male,No,0,1,0.0,6000,0.0,141.0,360.0,1,Urban,1.0
+LP001011,Male,Yes,2,1,1.0,5417,4196.0,267.0,360.0,1,Urban,1.0
+LP001013,Male,Yes,0,0,0.0,2333,1516.0,95.0,360.0,1,Urban,1.0
+LP001014,Male,Yes,3+,1,0.0,3036,2504.0,158.0,360.0,0,Semiurban,0.0
+LP001018,Male,Yes,2,1,0.0,4006,1526.0,168.0,360.0,1,Urban,1.0
+LP001020,Male,Yes,1,1,0.0,12841,10968.0,349.0,360.0,1,Semiurban,0.0
+LP001024,Male,Yes,2,1,0.0,3200,700.0,70.0,360.0,1,Urban,1.0
+LP001027,Male,Yes,2,1,0.0,2500,1840.0,109.0,360.0,1,Urban,1.0
+LP001028,Male,Yes,2,1,0.0,3073,8106.0,200.0,360.0,1,Urban,1.0
+LP001029,Male,No,0,1,0.0,1853,2840.0,114.0,360.0,1,Rural,0.0
+LP001030,Male,Yes,2,1,0.0,1299,1086.0,17.0,120.0,1,Urban,1.0
+LP001032,Male,No,0,1,0.0,4950,0.0,125.0,360.0,1,Urban,1.0
+LP001034,Male,No,1,0,0.0,3596,0.0,100.0,240.0,0,Urban,1.0
+LP001036,Female,No,0,1,0.0,3510,0.0,76.0,360.0,0,Urban,0.0
+LP001038,Male,Yes,0,0,0.0,4887,0.0,133.0,360.0,1,Rural,0.0
+LP001041,Male,Yes,0,1,0.0,2600,3500.0,115.0,,1,Urban,1.0
+LP001043,Male,Yes,0,0,0.0,7660,0.0,104.0,360.0,0,Urban,0.0
+LP001046,Male,Yes,1,1,0.0,5955,5625.0,315.0,360.0,1,Urban,1.0
+LP001047,Male,Yes,0,0,0.0,2600,1911.0,116.0,360.0,0,Semiurban,0.0
+LP001050,,Yes,2,0,0.0,3365,1917.0,112.0,360.0,0,Rural,0.0
+LP001052,Male,Yes,1,1,0.0,3717,2925.0,151.0,360.0,0,Semiurban,0.0
+LP001066,Male,Yes,0,1,1.0,9560,0.0,191.0,360.0,1,Semiurban,1.0
+LP001068,Male,Yes,0,1,0.0,2799,2253.0,122.0,360.0,1,Semiurban,1.0
+LP001073,Male,Yes,2,0,0.0,4226,1040.0,110.0,360.0,1,Urban,1.0
+LP001086,Male,No,0,0,0.0,1442,0.0,35.0,360.0,1,Urban,0.0
+LP001087,Female,No,2,1,0.0,3750,2083.0,120.0,360.0,1,Semiurban,1.0
+LP001091,Male,Yes,1,1,0.0,4166,3369.0,201.0,360.0,0,Urban,0.0
+LP001095,Male,No,0,1,0.0,3167,0.0,74.0,360.0,1,Urban,0.0
+LP001097,Male,No,1,1,1.0,4692,0.0,106.0,360.0,1,Rural,0.0
+LP001098,Male,Yes,0,1,0.0,3500,1667.0,114.0,360.0,1,Semiurban,1.0
+LP001100,Male,No,3+,1,0.0,12500,3000.0,320.0,360.0,1,Rural,0.0
+LP001106,Male,Yes,0,1,0.0,2275,2067.0,0.0,360.0,1,Urban,1.0
+LP001109,Male,Yes,0,1,0.0,1828,1330.0,100.0,,0,Urban,0.0
+LP001112,Female,Yes,0,1,0.0,3667,1459.0,144.0,360.0,1,Semiurban,1.0
+LP001114,Male,No,0,1,0.0,4166,7210.0,184.0,360.0,1,Urban,1.0
+LP001116,Male,No,0,0,0.0,3748,1668.0,110.0,360.0,1,Semiurban,1.0
+LP001119,Male,No,0,1,0.0,3600,0.0,80.0,360.0,1,Urban,0.0
+LP001120,Male,No,0,1,0.0,1800,1213.0,47.0,360.0,1,Urban,1.0
+LP001123,Male,Yes,0,1,0.0,2400,0.0,75.0,360.0,0,Urban,1.0
+LP001131,Male,Yes,0,1,0.0,3941,2336.0,134.0,360.0,1,Semiurban,1.0
+LP001136,Male,Yes,0,0,1.0,4695,0.0,96.0,,1,Urban,1.0
+LP001137,Female,No,0,1,0.0,3410,0.0,88.0,,1,Urban,1.0
+LP001138,Male,Yes,1,1,0.0,5649,0.0,44.0,360.0,1,Urban,1.0
+LP001144,Male,Yes,0,1,0.0,5821,0.0,144.0,360.0,1,Urban,1.0
+LP001146,Female,Yes,0,1,0.0,2645,3440.0,120.0,360.0,0,Urban,0.0
+LP001151,Female,No,0,1,0.0,4000,2275.0,144.0,360.0,1,Semiurban,1.0
+LP001155,Female,Yes,0,0,0.0,1928,1644.0,100.0,360.0,1,Semiurban,1.0
+LP001157,Female,No,0,1,0.0,3086,0.0,120.0,360.0,1,Semiurban,1.0
+LP001164,Female,No,0,1,0.0,4230,0.0,112.0,360.0,1,Semiurban,0.0
+LP001179,Male,Yes,2,1,0.0,4616,0.0,134.0,360.0,1,Urban,0.0
+LP001186,Female,Yes,1,1,1.0,11500,0.0,286.0,360.0,0,Urban,0.0
+LP001194,Male,Yes,2,1,0.0,2708,1167.0,97.0,360.0,1,Semiurban,1.0
+LP001195,Male,Yes,0,1,0.0,2132,1591.0,96.0,360.0,1,Semiurban,1.0
+LP001197,Male,Yes,0,1,0.0,3366,2200.0,135.0,360.0,1,Rural,0.0
+LP001198,Male,Yes,1,1,0.0,8080,2250.0,180.0,360.0,1,Urban,1.0
+LP001199,Male,Yes,2,0,0.0,3357,2859.0,144.0,360.0,1,Urban,1.0
+LP001205,Male,Yes,0,1,0.0,2500,3796.0,120.0,360.0,1,Urban,1.0
+LP001206,Male,Yes,3+,1,0.0,3029,0.0,99.0,360.0,1,Urban,1.0
+LP001207,Male,Yes,0,0,1.0,2609,3449.0,165.0,180.0,0,Rural,0.0
+LP001213,Male,Yes,1,1,0.0,4945,0.0,0.0,360.0,0,Rural,0.0
+LP001222,Female,No,0,1,0.0,4166,0.0,116.0,360.0,0,Semiurban,0.0
+LP001225,Male,Yes,0,1,0.0,5726,4595.0,258.0,360.0,1,Semiurban,0.0
+LP001228,Male,No,0,0,0.0,3200,2254.0,126.0,180.0,0,Urban,0.0
+LP001233,Male,Yes,1,1,0.0,10750,0.0,312.0,360.0,1,Urban,1.0
+LP001238,Male,Yes,3+,0,1.0,7100,0.0,125.0,60.0,1,Urban,1.0
+LP001241,Female,No,0,1,0.0,4300,0.0,136.0,360.0,0,Semiurban,0.0
+LP001243,Male,Yes,0,1,0.0,3208,3066.0,172.0,360.0,1,Urban,1.0
+LP001245,Male,Yes,2,0,1.0,1875,1875.0,97.0,360.0,1,Semiurban,1.0
+LP001248,Male,No,0,1,0.0,3500,0.0,81.0,300.0,1,Semiurban,1.0
+LP001250,Male,Yes,3+,0,0.0,4755,0.0,95.0,,0,Semiurban,0.0
+LP001253,Male,Yes,3+,1,1.0,5266,1774.0,187.0,360.0,1,Semiurban,1.0
+LP001255,Male,No,0,1,0.0,3750,0.0,113.0,480.0,1,Urban,0.0
+LP001256,Male,No,0,1,0.0,3750,4750.0,176.0,360.0,1,Urban,0.0
+LP001259,Male,Yes,1,1,1.0,1000,3022.0,110.0,360.0,1,Urban,0.0
+LP001263,Male,Yes,3+,1,0.0,3167,4000.0,180.0,300.0,0,Semiurban,0.0
+LP001264,Male,Yes,3+,0,1.0,3333,2166.0,130.0,360.0,0,Semiurban,1.0
+LP001265,Female,No,0,1,0.0,3846,0.0,111.0,360.0,1,Semiurban,1.0
+LP001266,Male,Yes,1,1,1.0,2395,0.0,0.0,360.0,1,Semiurban,1.0
+LP001267,Female,Yes,2,1,0.0,1378,1881.0,167.0,360.0,1,Urban,0.0
+LP001273,Male,Yes,0,1,0.0,6000,2250.0,265.0,360.0,0,Semiurban,0.0
+LP001275,Male,Yes,1,1,0.0,3988,0.0,50.0,240.0,1,Urban,1.0
+LP001279,Male,No,0,1,0.0,2366,2531.0,136.0,360.0,1,Semiurban,1.0
+LP001280,Male,Yes,2,0,0.0,3333,2000.0,99.0,360.0,0,Semiurban,1.0
+LP001282,Male,Yes,0,1,0.0,2500,2118.0,104.0,360.0,1,Semiurban,1.0
+LP001289,Male,No,0,1,0.0,8566,0.0,210.0,360.0,1,Urban,1.0
+LP001310,Male,Yes,0,1,0.0,5695,4167.0,175.0,360.0,1,Semiurban,1.0
+LP001316,Male,Yes,0,1,0.0,2958,2900.0,131.0,360.0,1,Semiurban,1.0
+LP001318,Male,Yes,2,1,0.0,6250,5654.0,188.0,180.0,1,Semiurban,1.0
+LP001319,Male,Yes,2,0,0.0,3273,1820.0,81.0,360.0,1,Urban,1.0
+LP001322,Male,No,0,1,0.0,4133,0.0,122.0,360.0,1,Semiurban,1.0
+LP001325,Male,No,0,0,0.0,3620,0.0,25.0,120.0,1,Semiurban,1.0
+LP001326,Male,No,0,1,0.0,6782,0.0,0.0,360.0,0,Urban,0.0
+LP001327,Female,Yes,0,1,0.0,2484,2302.0,137.0,360.0,1,Semiurban,1.0
+LP001333,Male,Yes,0,1,0.0,1977,997.0,50.0,360.0,1,Semiurban,1.0
+LP001334,Male,Yes,0,0,0.0,4188,0.0,115.0,180.0,1,Semiurban,1.0
+LP001343,Male,Yes,0,1,0.0,1759,3541.0,131.0,360.0,1,Semiurban,1.0
+LP001345,Male,Yes,2,0,0.0,4288,3263.0,133.0,180.0,1,Urban,1.0
+LP001349,Male,No,0,1,0.0,4843,3806.0,151.0,360.0,1,Semiurban,1.0
+LP001350,Male,Yes,,1,0.0,13650,0.0,0.0,360.0,1,Urban,1.0
+LP001356,Male,Yes,0,1,0.0,4652,3583.0,0.0,360.0,1,Semiurban,1.0
+LP001357,Male,,,1,0.0,3816,754.0,160.0,360.0,1,Urban,1.0
+LP001367,Male,Yes,1,1,0.0,3052,1030.0,100.0,360.0,1,Urban,1.0
+LP001369,Male,Yes,2,1,0.0,11417,1126.0,225.0,360.0,1,Urban,1.0
+LP001370,Male,No,0,0,0.0,7333,0.0,120.0,360.0,1,Rural,0.0
+LP001379,Male,Yes,2,1,0.0,3800,3600.0,216.0,360.0,0,Urban,0.0
+LP001384,Male,Yes,3+,0,0.0,2071,754.0,94.0,480.0,1,Semiurban,1.0
+LP001385,Male,No,0,1,0.0,5316,0.0,136.0,360.0,1,Urban,1.0
+LP001387,Female,Yes,0,1,0.0,2929,2333.0,139.0,360.0,1,Semiurban,1.0
+LP001391,Male,Yes,0,0,0.0,3572,4114.0,152.0,,0,Rural,0.0
+LP001392,Female,No,1,1,1.0,7451,0.0,0.0,360.0,1,Semiurban,1.0
+LP001398,Male,No,0,1,0.0,5050,0.0,118.0,360.0,1,Semiurban,1.0
+LP001401,Male,Yes,1,1,0.0,14583,0.0,185.0,180.0,1,Rural,1.0
+LP001404,Female,Yes,0,1,0.0,3167,2283.0,154.0,360.0,1,Semiurban,1.0
+LP001405,Male,Yes,1,1,0.0,2214,1398.0,85.0,360.0,0,Urban,1.0
+LP001421,Male,Yes,0,1,0.0,5568,2142.0,175.0,360.0,1,Rural,0.0
+LP001422,Female,No,0,1,0.0,10408,0.0,259.0,360.0,1,Urban,1.0
+LP001426,Male,Yes,,1,0.0,5667,2667.0,180.0,360.0,1,Rural,1.0
+LP001430,Female,No,0,1,0.0,4166,0.0,44.0,360.0,1,Semiurban,1.0
+LP001431,Female,No,0,1,0.0,2137,8980.0,137.0,360.0,0,Semiurban,1.0
+LP001432,Male,Yes,2,1,0.0,2957,0.0,81.0,360.0,1,Semiurban,1.0
+LP001439,Male,Yes,0,0,0.0,4300,2014.0,194.0,360.0,1,Rural,1.0
+LP001443,Female,No,0,1,0.0,3692,0.0,93.0,360.0,0,Rural,1.0
+LP001448,,Yes,3+,1,0.0,23803,0.0,370.0,360.0,1,Rural,1.0
+LP001449,Male,No,0,1,0.0,3865,1640.0,0.0,360.0,1,Rural,1.0
+LP001451,Male,Yes,1,1,1.0,10513,3850.0,160.0,180.0,0,Urban,0.0
+LP001465,Male,Yes,0,1,0.0,6080,2569.0,182.0,360.0,0,Rural,0.0
+LP001469,Male,No,0,1,1.0,20166,0.0,650.0,480.0,0,Urban,1.0
+LP001473,Male,No,0,1,0.0,2014,1929.0,74.0,360.0,1,Urban,1.0
+LP001478,Male,No,0,1,0.0,2718,0.0,70.0,360.0,1,Semiurban,1.0
+LP001482,Male,Yes,0,1,1.0,3459,0.0,25.0,120.0,1,Semiurban,1.0
+LP001487,Male,No,0,1,0.0,4895,0.0,102.0,360.0,1,Semiurban,1.0
+LP001488,Male,Yes,3+,1,0.0,4000,7750.0,290.0,360.0,1,Semiurban,0.0
+LP001489,Female,Yes,0,1,0.0,4583,0.0,84.0,360.0,1,Rural,0.0
+LP001491,Male,Yes,2,1,1.0,3316,3500.0,88.0,360.0,1,Urban,1.0
+LP001492,Male,No,0,1,0.0,14999,0.0,242.0,360.0,0,Semiurban,0.0
+LP001493,Male,Yes,2,0,0.0,4200,1430.0,129.0,360.0,1,Rural,0.0
+LP001497,Male,Yes,2,1,0.0,5042,2083.0,185.0,360.0,1,Rural,0.0
+LP001498,Male,No,0,1,0.0,5417,0.0,168.0,360.0,1,Urban,1.0
+LP001504,Male,No,0,1,1.0,6950,0.0,175.0,180.0,1,Semiurban,1.0
+LP001507,Male,Yes,0,1,0.0,2698,2034.0,122.0,360.0,1,Semiurban,1.0
+LP001508,Male,Yes,2,1,0.0,11757,0.0,187.0,180.0,1,Urban,1.0
+LP001514,Female,Yes,0,1,0.0,2330,4486.0,100.0,360.0,1,Semiurban,1.0
+LP001516,Female,Yes,2,1,0.0,14866,0.0,70.0,360.0,1,Urban,1.0
+LP001518,Male,Yes,1,1,0.0,1538,1425.0,30.0,360.0,1,Urban,1.0
+LP001519,Female,No,0,1,0.0,10000,1666.0,225.0,360.0,1,Rural,0.0
+LP001520,Male,Yes,0,1,0.0,4860,830.0,125.0,360.0,1,Semiurban,1.0
+LP001528,Male,No,0,1,0.0,6277,0.0,118.0,360.0,0,Rural,0.0
+LP001529,Male,Yes,0,1,1.0,2577,3750.0,152.0,360.0,1,Rural,1.0
+LP001531,Male,No,0,1,0.0,9166,0.0,244.0,360.0,1,Urban,0.0
+LP001532,Male,Yes,2,0,0.0,2281,0.0,113.0,360.0,1,Rural,0.0
+LP001535,Male,No,0,1,0.0,3254,0.0,50.0,360.0,1,Urban,1.0
+LP001536,Male,Yes,3+,1,0.0,39999,0.0,600.0,180.0,0,Semiurban,1.0
+LP001541,Male,Yes,1,1,0.0,6000,0.0,160.0,360.0,0,Rural,1.0
+LP001543,Male,Yes,1,1,0.0,9538,0.0,187.0,360.0,1,Urban,1.0
+LP001546,Male,No,0,1,0.0,2980,2083.0,120.0,360.0,1,Rural,1.0
+LP001552,Male,Yes,0,1,0.0,4583,5625.0,255.0,360.0,1,Semiurban,1.0
+LP001560,Male,Yes,0,0,0.0,1863,1041.0,98.0,360.0,1,Semiurban,1.0
+LP001562,Male,Yes,0,1,0.0,7933,0.0,275.0,360.0,1,Urban,0.0
+LP001565,Male,Yes,1,1,0.0,3089,1280.0,121.0,360.0,0,Semiurban,0.0
+LP001570,Male,Yes,2,1,0.0,4167,1447.0,158.0,360.0,1,Rural,1.0
+LP001572,Male,Yes,0,1,0.0,9323,0.0,75.0,180.0,1,Urban,1.0
+LP001574,Male,Yes,0,1,0.0,3707,3166.0,182.0,,1,Rural,1.0
+LP001577,Female,Yes,0,1,0.0,4583,0.0,112.0,360.0,1,Rural,0.0
+LP001578,Male,Yes,0,1,0.0,2439,3333.0,129.0,360.0,1,Rural,1.0
+LP001579,Male,No,0,1,0.0,2237,0.0,63.0,480.0,0,Semiurban,0.0
+LP001580,Male,Yes,2,1,0.0,8000,0.0,200.0,360.0,1,Semiurban,1.0
+LP001581,Male,Yes,0,0,0.0,1820,1769.0,95.0,360.0,1,Rural,1.0
+LP001585,,Yes,3+,1,0.0,51763,0.0,700.0,300.0,1,Urban,1.0
+LP001586,Male,Yes,3+,0,0.0,3522,0.0,81.0,180.0,1,Rural,0.0
+LP001594,Male,Yes,0,1,0.0,5708,5625.0,187.0,360.0,1,Semiurban,1.0
+LP001603,Male,Yes,0,0,1.0,4344,736.0,87.0,360.0,1,Semiurban,0.0
+LP001606,Male,Yes,0,1,0.0,3497,1964.0,116.0,360.0,1,Rural,1.0
+LP001608,Male,Yes,2,1,0.0,2045,1619.0,101.0,360.0,1,Rural,1.0
+LP001610,Male,Yes,3+,1,0.0,5516,11300.0,495.0,360.0,0,Semiurban,0.0
+LP001616,Male,Yes,1,1,0.0,3750,0.0,116.0,360.0,1,Semiurban,1.0
+LP001630,Male,No,0,0,0.0,2333,1451.0,102.0,480.0,0,Urban,0.0
+LP001633,Male,Yes,1,1,0.0,6400,7250.0,180.0,360.0,0,Urban,0.0
+LP001634,Male,No,0,1,0.0,1916,5063.0,67.0,360.0,0,Rural,0.0
+LP001636,Male,Yes,0,1,0.0,4600,0.0,73.0,180.0,1,Semiurban,1.0
+LP001637,Male,Yes,1,1,0.0,33846,0.0,260.0,360.0,1,Semiurban,0.0
+LP001639,Female,Yes,0,1,0.0,3625,0.0,108.0,360.0,1,Semiurban,1.0
+LP001640,Male,Yes,0,1,1.0,39147,4750.0,120.0,360.0,1,Semiurban,1.0
+LP001641,Male,Yes,1,1,1.0,2178,0.0,66.0,300.0,0,Rural,0.0
+LP001643,Male,Yes,0,1,0.0,2383,2138.0,58.0,360.0,0,Rural,1.0
+LP001644,,Yes,0,1,1.0,674,5296.0,168.0,360.0,1,Rural,1.0
+LP001647,Male,Yes,0,1,0.0,9328,0.0,188.0,180.0,1,Rural,1.0
+LP001653,Male,No,0,0,0.0,4885,0.0,48.0,360.0,1,Rural,1.0
+LP001656,Male,No,0,1,0.0,12000,0.0,164.0,360.0,1,Semiurban,0.0
+LP001657,Male,Yes,0,0,0.0,6033,0.0,160.0,360.0,1,Urban,0.0
+LP001658,Male,No,0,1,0.0,3858,0.0,76.0,360.0,1,Semiurban,1.0
+LP001664,Male,No,0,1,0.0,4191,0.0,120.0,360.0,1,Rural,1.0
+LP001665,Male,Yes,1,1,0.0,3125,2583.0,170.0,360.0,1,Semiurban,0.0
+LP001666,Male,No,0,1,0.0,8333,3750.0,187.0,360.0,1,Rural,1.0
+LP001669,Female,No,0,0,0.0,1907,2365.0,120.0,,1,Urban,1.0
+LP001671,Female,Yes,0,1,0.0,3416,2816.0,113.0,360.0,0,Semiurban,1.0
+LP001673,Male,No,0,1,1.0,11000,0.0,83.0,360.0,1,Urban,0.0
+LP001674,Male,Yes,1,0,0.0,2600,2500.0,90.0,360.0,1,Semiurban,1.0
+LP001677,Male,No,2,1,0.0,4923,0.0,166.0,360.0,0,Semiurban,1.0
+LP001682,Male,Yes,3+,0,0.0,3992,0.0,0.0,180.0,1,Urban,0.0
+LP001688,Male,Yes,1,0,0.0,3500,1083.0,135.0,360.0,1,Urban,1.0
+LP001691,Male,Yes,2,0,0.0,3917,0.0,124.0,360.0,1,Semiurban,1.0
+LP001692,Female,No,0,0,0.0,4408,0.0,120.0,360.0,1,Semiurban,1.0
+LP001693,Female,No,0,1,0.0,3244,0.0,80.0,360.0,1,Urban,1.0
+LP001698,Male,No,0,0,0.0,3975,2531.0,55.0,360.0,1,Rural,1.0
+LP001699,Male,No,0,1,0.0,2479,0.0,59.0,360.0,1,Urban,1.0
+LP001702,Male,No,0,1,0.0,3418,0.0,127.0,360.0,1,Semiurban,0.0
+LP001708,Female,No,0,1,0.0,10000,0.0,214.0,360.0,1,Semiurban,0.0
+LP001711,Male,Yes,3+,1,0.0,3430,1250.0,128.0,360.0,0,Semiurban,0.0
+LP001713,Male,Yes,1,1,1.0,7787,0.0,240.0,360.0,1,Urban,1.0
+LP001715,Male,Yes,3+,0,1.0,5703,0.0,130.0,360.0,1,Rural,1.0
+LP001716,Male,Yes,0,1,0.0,3173,3021.0,137.0,360.0,1,Urban,1.0
+LP001720,Male,Yes,3+,0,0.0,3850,983.0,100.0,360.0,1,Semiurban,1.0
+LP001722,Male,Yes,0,1,0.0,150,1800.0,135.0,360.0,1,Rural,0.0
+LP001726,Male,Yes,0,1,0.0,3727,1775.0,131.0,360.0,1,Semiurban,1.0
+LP001732,Male,Yes,2,1,0.0,5000,0.0,72.0,360.0,0,Semiurban,0.0
+LP001734,Female,Yes,2,1,0.0,4283,2383.0,127.0,360.0,0,Semiurban,1.0
+LP001736,Male,Yes,0,1,0.0,2221,0.0,60.0,360.0,0,Urban,0.0
+LP001743,Male,Yes,2,1,0.0,4009,1717.0,116.0,360.0,1,Semiurban,1.0
+LP001744,Male,No,0,1,0.0,2971,2791.0,144.0,360.0,1,Semiurban,1.0
+LP001749,Male,Yes,0,1,0.0,7578,1010.0,175.0,,1,Semiurban,1.0
+LP001750,Male,Yes,0,1,0.0,6250,0.0,128.0,360.0,1,Semiurban,1.0
+LP001751,Male,Yes,0,1,0.0,3250,0.0,170.0,360.0,1,Rural,0.0
+LP001754,Male,Yes,,0,1.0,4735,0.0,138.0,360.0,1,Urban,0.0
+LP001758,Male,Yes,2,1,0.0,6250,1695.0,210.0,360.0,1,Semiurban,1.0
+LP001760,Male,,,1,0.0,4758,0.0,158.0,480.0,1,Semiurban,1.0
+LP001761,Male,No,0,1,1.0,6400,0.0,200.0,360.0,1,Rural,1.0
+LP001765,Male,Yes,1,1,0.0,2491,2054.0,104.0,360.0,1,Semiurban,1.0
+LP001768,Male,Yes,0,1,0.0,3716,0.0,42.0,180.0,1,Rural,1.0
+LP001770,Male,No,0,0,0.0,3189,2598.0,120.0,,1,Rural,1.0
+LP001776,Female,No,0,1,0.0,8333,0.0,280.0,360.0,1,Semiurban,1.0
+LP001778,Male,Yes,1,1,0.0,3155,1779.0,140.0,360.0,1,Semiurban,1.0
+LP001784,Male,Yes,1,1,0.0,5500,1260.0,170.0,360.0,1,Rural,1.0
+LP001786,Male,Yes,0,1,0.0,5746,0.0,255.0,360.0,0,Urban,0.0
+LP001788,Female,No,0,1,1.0,3463,0.0,122.0,360.0,0,Urban,1.0
+LP001790,Female,No,1,1,0.0,3812,0.0,112.0,360.0,1,Rural,1.0
+LP001792,Male,Yes,1,1,0.0,3315,0.0,96.0,360.0,1,Semiurban,1.0
+LP001798,Male,Yes,2,1,0.0,5819,5000.0,120.0,360.0,1,Rural,1.0
+LP001800,Male,Yes,1,0,0.0,2510,1983.0,140.0,180.0,1,Urban,0.0
+LP001806,Male,No,0,1,0.0,2965,5701.0,155.0,60.0,1,Urban,1.0
+LP001807,Male,Yes,2,1,1.0,6250,1300.0,108.0,360.0,1,Rural,1.0
+LP001811,Male,Yes,0,0,0.0,3406,4417.0,123.0,360.0,1,Semiurban,1.0
+LP001813,Male,No,0,1,1.0,6050,4333.0,120.0,180.0,1,Urban,0.0
+LP001814,Male,Yes,2,1,0.0,9703,0.0,112.0,360.0,1,Urban,1.0
+LP001819,Male,Yes,1,0,0.0,6608,0.0,137.0,180.0,1,Urban,1.0
+LP001824,Male,Yes,1,1,0.0,2882,1843.0,123.0,480.0,1,Semiurban,1.0
+LP001825,Male,Yes,0,1,0.0,1809,1868.0,90.0,360.0,1,Urban,1.0
+LP001835,Male,Yes,0,0,0.0,1668,3890.0,201.0,360.0,0,Semiurban,0.0
+LP001836,Female,No,2,1,0.0,3427,0.0,138.0,360.0,1,Urban,0.0
+LP001841,Male,No,0,0,1.0,2583,2167.0,104.0,360.0,1,Rural,1.0
+LP001843,Male,Yes,1,0,0.0,2661,7101.0,279.0,180.0,1,Semiurban,1.0
+LP001844,Male,No,0,1,1.0,16250,0.0,192.0,360.0,0,Urban,0.0
+LP001846,Female,No,3+,1,0.0,3083,0.0,255.0,360.0,1,Rural,1.0
+LP001849,Male,No,0,0,0.0,6045,0.0,115.0,360.0,0,Rural,0.0
+LP001854,Male,Yes,3+,1,0.0,5250,0.0,94.0,360.0,1,Urban,0.0
+LP001859,Male,Yes,0,1,0.0,14683,2100.0,304.0,360.0,1,Rural,0.0
+LP001864,Male,Yes,3+,0,0.0,4931,0.0,128.0,360.0,0,Semiurban,0.0
+LP001865,Male,Yes,1,1,0.0,6083,4250.0,330.0,360.0,0,Urban,1.0
+LP001868,Male,No,0,1,0.0,2060,2209.0,134.0,360.0,1,Semiurban,1.0
+LP001870,Female,No,1,1,0.0,3481,0.0,155.0,36.0,1,Semiurban,0.0
+LP001871,Female,No,0,1,0.0,7200,0.0,120.0,360.0,1,Rural,1.0
+LP001872,Male,No,0,1,1.0,5166,0.0,128.0,360.0,1,Semiurban,1.0
+LP001875,Male,No,0,1,0.0,4095,3447.0,151.0,360.0,1,Rural,1.0
+LP001877,Male,Yes,2,1,0.0,4708,1387.0,150.0,360.0,1,Semiurban,1.0
+LP001882,Male,Yes,3+,1,0.0,4333,1811.0,160.0,360.0,0,Urban,1.0
+LP001883,Female,No,0,1,0.0,3418,0.0,135.0,360.0,1,Rural,0.0
+LP001884,Female,No,1,1,0.0,2876,1560.0,90.0,360.0,1,Urban,1.0
+LP001888,Female,No,0,1,0.0,3237,0.0,30.0,360.0,1,Urban,1.0
+LP001891,Male,Yes,0,1,0.0,11146,0.0,136.0,360.0,1,Urban,1.0
+LP001892,Male,No,0,1,0.0,2833,1857.0,126.0,360.0,1,Rural,1.0
+LP001894,Male,Yes,0,1,0.0,2620,2223.0,150.0,360.0,1,Semiurban,1.0
+LP001896,Male,Yes,2,1,0.0,3900,0.0,90.0,360.0,1,Semiurban,1.0
+LP001900,Male,Yes,1,1,0.0,2750,1842.0,115.0,360.0,1,Semiurban,1.0
+LP001903,Male,Yes,0,1,0.0,3993,3274.0,207.0,360.0,1,Semiurban,1.0
+LP001904,Male,Yes,0,1,0.0,3103,1300.0,80.0,360.0,1,Urban,1.0
+LP001907,Male,Yes,0,1,0.0,14583,0.0,436.0,360.0,1,Semiurban,1.0
+LP001908,Female,Yes,0,0,0.0,4100,0.0,124.0,360.0,0,Rural,1.0
+LP001910,Male,No,1,0,1.0,4053,2426.0,158.0,360.0,0,Urban,0.0
+LP001914,Male,Yes,0,1,0.0,3927,800.0,112.0,360.0,1,Semiurban,1.0
+LP001915,Male,Yes,2,1,0.0,2301,985.7999878,78.0,180.0,1,Urban,1.0
+LP001917,Female,No,0,1,0.0,1811,1666.0,54.0,360.0,1,Urban,1.0
+LP001922,Male,Yes,0,1,0.0,20667,0.0,0.0,360.0,1,Rural,0.0
+LP001924,Male,No,0,1,0.0,3158,3053.0,89.0,360.0,1,Rural,1.0
+LP001925,Female,No,0,1,1.0,2600,1717.0,99.0,300.0,1,Semiurban,0.0
+LP001926,Male,Yes,0,1,0.0,3704,2000.0,120.0,360.0,1,Rural,1.0
+LP001931,Female,No,0,1,0.0,4124,0.0,115.0,360.0,1,Semiurban,1.0
+LP001935,Male,No,0,1,0.0,9508,0.0,187.0,360.0,1,Rural,1.0
+LP001936,Male,Yes,0,1,0.0,3075,2416.0,139.0,360.0,1,Rural,1.0
+LP001938,Male,Yes,2,1,0.0,4400,0.0,127.0,360.0,0,Semiurban,0.0
+LP001940,Male,Yes,2,1,0.0,3153,1560.0,134.0,360.0,1,Urban,1.0
+LP001945,Female,No,,1,0.0,5417,0.0,143.0,480.0,0,Urban,0.0
+LP001947,Male,Yes,0,1,0.0,2383,3334.0,172.0,360.0,1,Semiurban,1.0
+LP001949,Male,Yes,3+,1,0.0,4416,1250.0,110.0,360.0,1,Urban,1.0
+LP001953,Male,Yes,1,1,0.0,6875,0.0,200.0,360.0,1,Semiurban,1.0
+LP001954,Female,Yes,1,1,0.0,4666,0.0,135.0,360.0,1,Urban,1.0
+LP001955,Female,No,0,1,0.0,5000,2541.0,151.0,480.0,1,Rural,0.0
+LP001963,Male,Yes,1,1,0.0,2014,2925.0,113.0,360.0,1,Urban,0.0
+LP001964,Male,Yes,0,0,0.0,1800,2934.0,93.0,360.0,0,Urban,0.0
+LP001972,Male,Yes,,0,0.0,2875,1750.0,105.0,360.0,1,Semiurban,1.0
+LP001974,Female,No,0,1,0.0,5000,0.0,132.0,360.0,1,Rural,1.0
+LP001977,Male,Yes,1,1,0.0,1625,1803.0,96.0,360.0,1,Urban,1.0
+LP001978,Male,No,0,1,0.0,4000,2500.0,140.0,360.0,1,Rural,1.0
+LP001990,Male,No,0,0,0.0,2000,0.0,0.0,360.0,1,Urban,0.0
+LP001993,Female,No,0,1,0.0,3762,1666.0,135.0,360.0,1,Rural,1.0
+LP001994,Female,No,0,1,0.0,2400,1863.0,104.0,360.0,0,Urban,0.0
+LP001996,Male,No,0,1,0.0,20233,0.0,480.0,360.0,1,Rural,0.0
+LP001998,Male,Yes,2,0,0.0,7667,0.0,185.0,360.0,0,Rural,1.0
+LP002002,Female,No,0,1,0.0,2917,0.0,84.0,360.0,1,Semiurban,1.0
+LP002004,Male,No,0,0,0.0,2927,2405.0,111.0,360.0,1,Semiurban,1.0
+LP002006,Female,No,0,1,0.0,2507,0.0,56.0,360.0,1,Rural,1.0
+LP002008,Male,Yes,2,1,1.0,5746,0.0,144.0,84.0,0,Rural,1.0
+LP002024,,Yes,0,1,0.0,2473,1843.0,159.0,360.0,1,Rural,0.0
+LP002031,Male,Yes,1,0,0.0,3399,1640.0,111.0,180.0,1,Urban,1.0
+LP002035,Male,Yes,2,1,0.0,3717,0.0,120.0,360.0,1,Semiurban,1.0
+LP002036,Male,Yes,0,1,0.0,2058,2134.0,88.0,360.0,0,Urban,1.0
+LP002043,Female,No,1,1,0.0,3541,0.0,112.0,360.0,0,Semiurban,1.0
+LP002050,Male,Yes,1,1,1.0,10000,0.0,155.0,360.0,1,Rural,0.0
+LP002051,Male,Yes,0,1,0.0,2400,2167.0,115.0,360.0,1,Semiurban,1.0
+LP002053,Male,Yes,3+,1,0.0,4342,189.0,124.0,360.0,1,Semiurban,1.0
+LP002054,Male,Yes,2,0,0.0,3601,1590.0,0.0,360.0,1,Rural,1.0
+LP002055,Female,No,0,1,0.0,3166,2985.0,132.0,360.0,0,Rural,1.0
+LP002065,Male,Yes,3+,1,0.0,15000,0.0,300.0,360.0,1,Rural,1.0
+LP002067,Male,Yes,1,1,1.0,8666,4983.0,376.0,360.0,0,Rural,0.0
+LP002068,Male,No,0,1,0.0,4917,0.0,130.0,360.0,0,Rural,1.0
+LP002082,Male,Yes,0,1,1.0,5818,2160.0,184.0,360.0,1,Semiurban,1.0
+LP002086,Female,Yes,0,1,0.0,4333,2451.0,110.0,360.0,1,Urban,0.0
+LP002087,Female,No,0,1,0.0,2500,0.0,67.0,360.0,1,Urban,1.0
+LP002097,Male,No,1,1,0.0,4384,1793.0,117.0,360.0,1,Urban,1.0
+LP002098,Male,No,0,1,0.0,2935,0.0,98.0,360.0,1,Semiurban,1.0
+LP002100,Male,No,,1,0.0,2833,0.0,71.0,360.0,1,Urban,1.0
+LP002101,Male,Yes,0,1,0.0,63337,0.0,490.0,180.0,1,Urban,1.0
+LP002103,,Yes,1,1,1.0,9833,1833.0,182.0,180.0,1,Urban,1.0
+LP002106,Male,Yes,,1,1.0,5503,4490.0,70.0,,1,Semiurban,1.0
+LP002110,Male,Yes,1,1,0.0,5250,688.0,160.0,360.0,1,Rural,1.0
+LP002112,Male,Yes,2,1,1.0,2500,4600.0,176.0,360.0,1,Rural,1.0
+LP002113,Female,No,3+,0,0.0,1830,0.0,0.0,360.0,0,Urban,0.0
+LP002114,Female,No,0,1,0.0,4160,0.0,71.0,360.0,1,Semiurban,1.0
+LP002115,Male,Yes,3+,0,0.0,2647,1587.0,173.0,360.0,1,Rural,0.0
+LP002116,Female,No,0,1,0.0,2378,0.0,46.0,360.0,1,Rural,0.0
+LP002119,Male,Yes,1,0,0.0,4554,1229.0,158.0,360.0,1,Urban,1.0
+LP002126,Male,Yes,3+,0,0.0,3173,0.0,74.0,360.0,1,Semiurban,1.0
+LP002128,Male,Yes,2,1,0.0,2583,2330.0,125.0,360.0,1,Rural,1.0
+LP002129,Male,Yes,0,1,0.0,2499,2458.0,160.0,360.0,1,Semiurban,1.0
+LP002130,Male,Yes,,0,0.0,3523,3230.0,152.0,360.0,0,Rural,0.0
+LP002131,Male,Yes,2,0,0.0,3083,2168.0,126.0,360.0,1,Urban,1.0
+LP002137,Male,Yes,0,1,0.0,6333,4583.0,259.0,360.0,0,Semiurban,1.0
+LP002138,Male,Yes,0,1,0.0,2625,6250.0,187.0,360.0,1,Rural,1.0
+LP002139,Male,Yes,0,1,0.0,9083,0.0,228.0,360.0,1,Semiurban,1.0
+LP002140,Male,No,0,1,0.0,8750,4167.0,308.0,360.0,1,Rural,0.0
+LP002141,Male,Yes,3+,1,0.0,2666,2083.0,95.0,360.0,1,Rural,1.0
+LP002142,Female,Yes,0,1,1.0,5500,0.0,105.0,360.0,0,Rural,0.0
+LP002143,Female,Yes,0,1,0.0,2423,505.0,130.0,360.0,1,Semiurban,1.0
+LP002144,Female,No,,1,0.0,3813,0.0,116.0,180.0,1,Urban,1.0
+LP002149,Male,Yes,2,1,0.0,8333,3167.0,165.0,360.0,1,Rural,1.0
+LP002151,Male,Yes,1,1,0.0,3875,0.0,67.0,360.0,1,Urban,0.0
+LP002158,Male,Yes,0,0,0.0,3000,1666.0,100.0,480.0,0,Urban,0.0
+LP002160,Male,Yes,3+,1,0.0,5167,3167.0,200.0,360.0,1,Semiurban,1.0
+LP002161,Female,No,1,1,0.0,4723,0.0,81.0,360.0,1,Semiurban,0.0
+LP002170,Male,Yes,2,1,0.0,5000,3667.0,236.0,360.0,1,Semiurban,1.0
+LP002175,Male,Yes,0,1,0.0,4750,2333.0,130.0,360.0,1,Urban,1.0
+LP002178,Male,Yes,0,1,0.0,3013,3033.0,95.0,300.0,0,Urban,1.0
+LP002180,Male,No,0,1,1.0,6822,0.0,141.0,360.0,1,Rural,1.0
+LP002181,Male,No,0,0,0.0,6216,0.0,133.0,360.0,1,Rural,0.0
+LP002187,Male,No,0,1,0.0,2500,0.0,96.0,480.0,1,Semiurban,0.0
+LP002188,Male,No,0,1,0.0,5124,0.0,124.0,,0,Rural,0.0
+LP002190,Male,Yes,1,1,0.0,6325,0.0,175.0,360.0,1,Semiurban,1.0
+LP002191,Male,Yes,0,1,0.0,19730,5266.0,570.0,360.0,1,Rural,0.0
+LP002194,Female,No,0,1,1.0,15759,0.0,55.0,360.0,1,Semiurban,1.0
+LP002197,Male,Yes,2,1,0.0,5185,0.0,155.0,360.0,1,Semiurban,1.0
+LP002201,Male,Yes,2,1,1.0,9323,7873.0,380.0,300.0,1,Rural,1.0
+LP002205,Male,No,1,1,0.0,3062,1987.0,111.0,180.0,0,Urban,0.0
+LP002209,Female,No,0,1,0.0,2764,1459.0,110.0,360.0,1,Urban,1.0
+LP002211,Male,Yes,0,1,0.0,4817,923.0,120.0,180.0,1,Urban,1.0
+LP002219,Male,Yes,3+,1,0.0,8750,4996.0,130.0,360.0,1,Rural,1.0
+LP002223,Male,Yes,0,1,0.0,4310,0.0,130.0,360.0,0,Semiurban,1.0
+LP002224,Male,No,0,1,0.0,3069,0.0,71.0,480.0,1,Urban,0.0
+LP002225,Male,Yes,2,1,0.0,5391,0.0,130.0,360.0,1,Urban,1.0
+LP002226,Male,Yes,0,1,0.0,3333,2500.0,128.0,360.0,1,Semiurban,1.0
+LP002229,Male,No,0,1,0.0,5941,4232.0,296.0,360.0,1,Semiurban,1.0
+LP002231,Female,No,0,1,0.0,6000,0.0,156.0,360.0,1,Urban,1.0
+LP002234,Male,No,0,1,1.0,7167,0.0,128.0,360.0,1,Urban,1.0
+LP002236,Male,Yes,2,1,0.0,4566,0.0,100.0,360.0,1,Urban,0.0
+LP002237,Male,No,1,1,0.0,3667,0.0,113.0,180.0,1,Urban,1.0
+LP002239,Male,No,0,0,0.0,2346,1600.0,132.0,360.0,1,Semiurban,1.0
+LP002243,Male,Yes,0,0,0.0,3010,3136.0,0.0,360.0,0,Urban,0.0
+LP002244,Male,Yes,0,1,0.0,2333,2417.0,136.0,360.0,1,Urban,1.0
+LP002250,Male,Yes,0,1,0.0,5488,0.0,125.0,360.0,1,Rural,1.0
+LP002255,Male,No,3+,1,0.0,9167,0.0,185.0,360.0,1,Rural,1.0
+LP002262,Male,Yes,3+,1,0.0,9504,0.0,275.0,360.0,1,Rural,1.0
+LP002263,Male,Yes,0,1,0.0,2583,2115.0,120.0,360.0,0,Urban,1.0
+LP002265,Male,Yes,2,0,0.0,1993,1625.0,113.0,180.0,1,Semiurban,1.0
+LP002266,Male,Yes,2,1,0.0,3100,1400.0,113.0,360.0,1,Urban,1.0
+LP002272,Male,Yes,2,1,0.0,3276,484.0,135.0,360.0,0,Semiurban,1.0
+LP002277,Female,No,0,1,0.0,3180,0.0,71.0,360.0,0,Urban,0.0
+LP002281,Male,Yes,0,1,0.0,3033,1459.0,95.0,360.0,1,Urban,1.0
+LP002284,Male,No,0,0,0.0,3902,1666.0,109.0,360.0,1,Rural,1.0
+LP002287,Female,No,0,1,0.0,1500,1800.0,103.0,360.0,0,Semiurban,0.0
+LP002288,Male,Yes,2,0,0.0,2889,0.0,45.0,180.0,0,Urban,0.0
+LP002296,Male,No,0,0,0.0,2755,0.0,65.0,300.0,1,Rural,0.0
+LP002297,Male,No,0,1,0.0,2500,20000.0,103.0,360.0,1,Semiurban,1.0
+LP002300,Female,No,0,0,0.0,1963,0.0,53.0,360.0,1,Semiurban,1.0
+LP002301,Female,No,0,1,1.0,7441,0.0,194.0,360.0,1,Rural,0.0
+LP002305,Female,No,0,1,0.0,4547,0.0,115.0,360.0,1,Semiurban,1.0
+LP002308,Male,Yes,0,0,0.0,2167,2400.0,115.0,360.0,1,Urban,1.0
+LP002314,Female,No,0,0,0.0,2213,0.0,66.0,360.0,1,Rural,1.0
+LP002315,Male,Yes,1,1,0.0,8300,0.0,152.0,300.0,0,Semiurban,0.0
+LP002317,Male,Yes,3+,1,0.0,81000,0.0,360.0,360.0,0,Rural,0.0
+LP002318,Female,No,1,0,1.0,3867,0.0,62.0,360.0,1,Semiurban,0.0
+LP002319,Male,Yes,0,1,0.0,6256,0.0,160.0,360.0,0,Urban,1.0
+LP002328,Male,Yes,0,0,0.0,6096,0.0,218.0,360.0,0,Rural,0.0
+LP002332,Male,Yes,0,0,0.0,2253,2033.0,110.0,360.0,1,Rural,1.0
+LP002335,Female,Yes,0,0,0.0,2149,3237.0,178.0,360.0,0,Semiurban,0.0
+LP002337,Female,No,0,1,0.0,2995,0.0,60.0,360.0,1,Urban,1.0
+LP002341,Female,No,1,1,0.0,2600,0.0,160.0,360.0,1,Urban,0.0
+LP002342,Male,Yes,2,1,1.0,1600,20000.0,239.0,360.0,1,Urban,0.0
+LP002345,Male,Yes,0,1,0.0,1025,2773.0,112.0,360.0,1,Rural,1.0
+LP002347,Male,Yes,0,1,0.0,3246,1417.0,138.0,360.0,1,Semiurban,1.0
+LP002348,Male,Yes,0,1,0.0,5829,0.0,138.0,360.0,1,Rural,1.0
+LP002357,Female,No,0,0,0.0,2720,0.0,80.0,,0,Urban,0.0
+LP002361,Male,Yes,0,1,0.0,1820,1719.0,100.0,360.0,1,Urban,1.0
+LP002362,Male,Yes,1,1,0.0,7250,1667.0,110.0,,0,Urban,0.0
+LP002364,Male,Yes,0,1,0.0,14880,0.0,96.0,360.0,1,Semiurban,1.0
+LP002366,Male,Yes,0,1,0.0,2666,4300.0,121.0,360.0,1,Rural,1.0
+LP002367,Female,No,1,0,0.0,4606,0.0,81.0,360.0,1,Rural,0.0
+LP002368,Male,Yes,2,1,0.0,5935,0.0,133.0,360.0,1,Semiurban,1.0
+LP002369,Male,Yes,0,1,0.0,2920,16.12000084,87.0,360.0,1,Rural,1.0
+LP002370,Male,No,0,0,0.0,2717,0.0,60.0,180.0,1,Urban,1.0
+LP002377,Female,No,1,1,1.0,8624,0.0,150.0,360.0,1,Semiurban,1.0
+LP002379,Male,No,0,1,0.0,6500,0.0,105.0,360.0,0,Rural,0.0
+LP002386,Male,No,0,1,0.0,12876,0.0,405.0,360.0,1,Semiurban,1.0
+LP002387,Male,Yes,0,1,0.0,2425,2340.0,143.0,360.0,1,Semiurban,1.0
+LP002390,Male,No,0,1,0.0,3750,0.0,100.0,360.0,1,Urban,1.0
+LP002393,Female,,,1,0.0,10047,0.0,0.0,240.0,1,Semiurban,1.0
+LP002398,Male,No,0,1,0.0,1926,1851.0,50.0,360.0,1,Semiurban,1.0
+LP002401,Male,Yes,0,1,0.0,2213,1125.0,0.0,360.0,1,Urban,1.0
+LP002403,Male,No,0,1,1.0,10416,0.0,187.0,360.0,0,Urban,0.0
+LP002407,Female,Yes,0,0,1.0,7142,0.0,138.0,360.0,1,Rural,1.0
+LP002408,Male,No,0,1,0.0,3660,5064.0,187.0,360.0,1,Semiurban,1.0
+LP002409,Male,Yes,0,1,0.0,7901,1833.0,180.0,360.0,1,Rural,1.0
+LP002418,Male,No,3+,0,0.0,4707,1993.0,148.0,360.0,1,Semiurban,1.0
+LP002422,Male,No,1,1,0.0,37719,0.0,152.0,360.0,1,Semiurban,1.0
+LP002424,Male,Yes,0,1,0.0,7333,8333.0,175.0,300.0,0,Rural,1.0
+LP002429,Male,Yes,1,1,1.0,3466,1210.0,130.0,360.0,1,Rural,1.0
+LP002434,Male,Yes,2,0,0.0,4652,0.0,110.0,360.0,1,Rural,1.0
+LP002435,Male,Yes,0,1,0.0,3539,1376.0,55.0,360.0,1,Rural,0.0
+LP002443,Male,Yes,2,1,0.0,3340,1710.0,150.0,360.0,0,Rural,0.0
+LP002444,Male,No,1,0,1.0,2769,1542.0,190.0,360.0,0,Semiurban,0.0
+LP002446,Male,Yes,2,0,0.0,2309,1255.0,125.0,360.0,0,Rural,0.0
+LP002447,Male,Yes,2,0,0.0,1958,1456.0,60.0,300.0,0,Urban,1.0
+LP002448,Male,Yes,0,1,0.0,3948,1733.0,149.0,360.0,0,Rural,0.0
+LP002449,Male,Yes,0,1,0.0,2483,2466.0,90.0,180.0,0,Rural,1.0
+LP002453,Male,No,0,1,1.0,7085,0.0,84.0,360.0,1,Semiurban,1.0
+LP002455,Male,Yes,2,1,0.0,3859,0.0,96.0,360.0,1,Semiurban,1.0
+LP002459,Male,Yes,0,1,0.0,4301,0.0,118.0,360.0,1,Urban,1.0
+LP002467,Male,Yes,0,1,0.0,3708,2569.0,173.0,360.0,1,Urban,0.0
+LP002472,Male,No,2,1,0.0,4354,0.0,136.0,360.0,1,Rural,1.0
+LP002473,Male,Yes,0,1,0.0,8334,0.0,160.0,360.0,1,Semiurban,0.0
+LP002478,,Yes,0,1,1.0,2083,4083.0,160.0,360.0,0,Semiurban,1.0
+LP002484,Male,Yes,3+,1,0.0,7740,0.0,128.0,180.0,1,Urban,1.0
+LP002487,Male,Yes,0,1,0.0,3015,2188.0,153.0,360.0,1,Rural,1.0
+LP002489,Female,No,1,0,0.0,5191,0.0,132.0,360.0,1,Semiurban,1.0
+LP002493,Male,No,0,1,0.0,4166,0.0,98.0,360.0,0,Semiurban,0.0
+LP002494,Male,No,0,1,0.0,6000,0.0,140.0,360.0,1,Rural,1.0
+LP002500,Male,Yes,3+,0,0.0,2947,1664.0,70.0,180.0,0,Urban,0.0
+LP002501,,Yes,0,1,0.0,16692,0.0,110.0,360.0,1,Semiurban,1.0
+LP002502,Female,Yes,2,0,0.0,210,2917.0,98.0,360.0,1,Semiurban,1.0
+LP002505,Male,Yes,0,1,0.0,4333,2451.0,110.0,360.0,1,Urban,0.0
+LP002515,Male,Yes,1,1,1.0,3450,2079.0,162.0,360.0,1,Semiurban,1.0
+LP002517,Male,Yes,1,0,0.0,2653,1500.0,113.0,180.0,0,Rural,0.0
+LP002519,Male,Yes,3+,1,0.0,4691,0.0,100.0,360.0,1,Semiurban,1.0
+LP002522,Female,No,0,1,1.0,2500,0.0,93.0,360.0,0,Urban,1.0
+LP002524,Male,No,2,1,0.0,5532,4648.0,162.0,360.0,1,Rural,1.0
+LP002527,Male,Yes,2,1,1.0,16525,1014.0,150.0,360.0,1,Rural,1.0
+LP002529,Male,Yes,2,1,0.0,6700,1750.0,230.0,300.0,1,Semiurban,1.0
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+LP002757,Female,Yes,0,0,0.0,3017,663.0,102.0,360.0,0,Semiurban,1.0
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+LP002768,Male,No,0,0,0.0,3358,0.0,80.0,36.0,1,Semiurban,0.0
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+LP002893,Male,No,0,1,0.0,1836,33837.0,90.0,360.0,1,Urban,0.0
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+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
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abanin_daniil_lab_5/README.md

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

abanin_daniil_lab_5/lab5.py

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

abanin_daniil_lab_5/loan.csv

@@ -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
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+LP001006,Male,1.0,0,0,0.0,2583,2358.0,120.0,360.0,1,Urban,1.0
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+LP002847,Male,1.0,,1,0.0,5116,1451.0,165.0,360.0,0,Urban,0.0
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+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
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+LP002925,,0.0,0,1,0.0,4750,0.0,94.0,360.0,1,Semiurban,1.0
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+LP002931,Male,1.0,2,1,1.0,6000,0.0,205.0,240.0,1,Semiurban,0.0
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+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
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+LP002990,Female,0.0,0,1,1.0,4583,0.0,133.0,360.0,0,Semiurban,0.0

alexandrov_dmitrii_lab_1/lab1.py

@@ -0,0 +1,56 @@
+import random
+from matplotlib import pyplot as plt
+from matplotlib.colors import ListedColormap
+from sklearn.datasets import make_moons
+from sklearn.linear_model import LinearRegression, Ridge
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.pipeline import Pipeline
+
+rs = random.randrange(50)
+
+X, y = make_moons(n_samples=250, noise=0.3, random_state=rs)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)
+
+figure = plt.figure(1, figsize=(16, 9))
+axis = figure.subplots(4, 3)
+cm = ListedColormap(['#FF0000', "#0000FF"])
+arr_res = list(range(len(y_test)))
+X_scale = list(range(len(y_test)))
+
+
+def test(col, model):
+    global axis
+    global arr_res
+    global X_test
+    global X_train
+    global y_train
+    global y_test
+
+    model.fit(X_train, y_train)
+    res_y = model.predict(X_test)
+    print(model.score(X_test, y_test))
+
+    axis[0, col].scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm)
+    axis[1, col].scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm)
+    axis[2, col].scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm)
+    axis[2, col].scatter(X_test[:, 0], X_test[:, 1], c=res_y, cmap=cm)
+    axis[3, col].plot([i for i in range(len(res_y))], y_test, c="g")
+    axis[3, col].plot([i for i in range(len(res_y))], res_y, c="r")
+
+
+def start():
+    lin = LinearRegression()
+    poly = Pipeline([('poly', PolynomialFeatures(degree=3)),
+                     ('linear', LinearRegression())])
+    ridge = Pipeline([('poly', PolynomialFeatures(degree=3)),
+                      ('ridge', Ridge(alpha=1.0))])
+
+    test(0, lin)
+    test(1, poly)
+    test(2, ridge)
+
+    plt.show()
+
+
+start()

alexandrov_dmitrii_lab_1/readme.md

@@ -0,0 +1,46 @@
+## Задание
+Сгенерировать определенный тип данных и сравнить на нем 3 модели. Построить графики, отобразить качество моделей, объяснить полученные результаты.
+Вариант 1.
+Данные: make_moons (noise=0.3, random_state=rs)
+Модели:
+· Линейная регрессия
+· Полиномиальная регрессия (со степенью 3)
+· Гребневая полиномиальная регрессия (со степенью 3, alpha = 1.0) 
+
+### Запуск программы
+Файл lab1.py содержит и запускает программу, аргументов и настройки ~~вроде~~ не требует, 
+
+### Описание программы
+Генерирует один из 50 наборов данных, показывает окно с графиками и пишет оценку моделей обучения по заданию. 
+Использует библиотеки matplotlib для демонстрации графиков и sklearn для создания и использования моделей. 
+
+### Результаты тестирования
+Для различных значений rs результаты следующие:
+значение - линейная - полиномиальная - гребневая полиномиальная
+1 - 0.54 - 0.08 - 0.35
+2 - 0.62 - 0.58 - 0.63
+3 - 0.6 - 0.67 - 0.65
+4 - 0.52 - 0.46 - 0.5
+5 - 0.4 - 0.42 - 0.44
+Из данных результатов можно заключить, что чёткой зависимости точности от выбранной модели нет.
+
+Однако, после этого я добавил в генератор данных число значений: 500. Результаты оказались более детерминированными:
+значение - линейная - полиномиальная - гребневая полиномиальная
+1 - 0.54 - 0.63 - 0.63
+2 - 0.52 - 0.63 - 0.62
+3 - 0.56 - 0.64 - 0.64
+4 - 0.5 - 0.63 - 0.62
+5 - 0.5 - 0.52 - 0.53
+Из данных результатов можно заключить, что в общем случае модель линейной регрессии уступает полиномиальным. Гребневая полиномиальная регрессия чаще уступала обычной полиномиальной, однако в незначительном количестве ситуаций была оценена выше - но во всех случаях результаты были близки, поэтому можно с уверенностью предположить, что результаты идентичны и различаются по воле шума обучения.
+
+После изучения число значений в генераторе заменено на 250, поскольку графики становились неразличимыми^
+значение - линейная - полиномиальная - гребневая полиномиальная
+1 - 0.48 - 0.54 - 0.54
+2 - 0.5 - 0.56 - 0.56
+3 - 0.57 - 0.6 - 0.6
+4 - 0.57 - 0.66 - 0.68
+5 - 0.49 - 0.54 - 0.55
+По данным результатам видно, что в большинстве ситуаций уже гребневая полиномиальная регрессия показывает лучшую точность.
+
+Результаты объясняются следующим образом: 
+Линейная регрессия будучи математически прямой плохо отражает сложные функции и нелинейные зависимости, в то время как полиномиальная регрессия способна отражать перегибы и изменяющиеся в зависимости от меры значений зависимости. Гребневая полиномиальная вышла идентичной простой полиномиальной из-за одинаковых настроек - обе они по заданию имеют третью степень, а гребневая регрессия имеет слишком малый параметр alpha, что результирует в малом эффекте гребневой функции.

alexandrov_dmitrii_lab_2/lab2.py

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

alexandrov_dmitrii_lab_2/readme.md

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

alexandrov_dmitrii_lab_3/lab3.py

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

alexandrov_dmitrii_lab_3/readme.md

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

alexandrov_dmitrii_lab_3/titanic_data.csv

@@ -0,0 +1,892 @@
+PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
+1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
+2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
+3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
+14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
+17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
+18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
+19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
+20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
+21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
+22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
+23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
+24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
+25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
+26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
+27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
+28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
+29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
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+31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
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+34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
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+84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S
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+86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S
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+112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
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+115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
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+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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+128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S
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+131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
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+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
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+814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6,4,2,347082,31.275,,S
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+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
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+821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S
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+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
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+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
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+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
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+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

alexandrov_dmitrii_lab_4/lab4.py

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

alexandrov_dmitrii_lab_4/readme.md

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

alexandrov_dmitrii_lab_5/lab5.py

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

alexandrov_dmitrii_lab_5/readme.md

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

alexandrov_dmitrii_lab_6/lab6.py

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

alexandrov_dmitrii_lab_6/readme.md

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

alexandrov_dmitrii_lab_7/lab7.py

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

alexandrov_dmitrii_lab_7/readme.md

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

almukhammetov_bulat_lab_1/README.md

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

almukhammetov_bulat_lab_1/lab1.py

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

almukhammetov_bulat_lab_2/README.md

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

almukhammetov_bulat_lab_2/lab2.py

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

antonov_dmitry_lab_1/README.md

@@ -0,0 +1,97 @@
+# Лаб 1
+
+Работа с типовыми наборами данных и различными моделями
+
+# Вариант 3
+
+Данные: make_classification (n_samples=500, n_features=2,
+n_redundant=0, n_informative=2, random_state=rs, n_clusters_per_class=1)
+
+# Запуск
+
+Выполнением скрипта файла (вывод в консоль + рисует графики).
+
+# Модели:
+
+1. Линейная регрессия
+1. Полиномиальная регрессия (со степенью 3)
+1. Гребневая полиномиальная регрессия (со степенью 3, alpha = 1.0)
+
+# Графики
+
+<div>
+Качество каждой модели может быть оценено на основе среднеквадратичной ошибки (MSE). 
+Более низкая MSE указывает на лучшее соответствие данным. 
+Однако выбор модели зависит от набора данных и лежащей в основе взаимосвязи между объектами и целевой переменной.
+
+Линейная регрессия: Линейная регрессия предполагает линейную зависимость между признаками и целевой переменной.
+Это хорошо работает, когда взаимосвязь линейна, а шум в наборе данных минимален.
+Лучше всего сработала на наборе лун. Хуже всего на кругах.
+На линейном наборе показала себя на равне с остальными.
+
+Полиномиальная и гребневая показали примерно одинаково на всех наборах.
+
+Полиномиальная регрессия (степень=3):
+Полиномиальная регрессия обеспечивает более гибкую подгонку за счет полинома более высокого порядка(кубическая кривая).
+Она может выявить более сложные взаимосвязи между объектами и целевой переменной.
+Она может сработать лучше, чем линейная регрессия, если истинная взаимосвязь нелинейна.
+
+Гребневая регрессия (степень= 3, альфа=1,0):
+В случае полиномиальной регрессии с регуляризацией (альфа=1,0) модель добавляет коэффициент регуляризации
+для управления сложностью обучения. Регуляризация помогает предотвратить переобучение, когда набор
+данных содержит шум или когда он ограничен.
+</div>
+
+<p>
+    <div>Набор лун (moon_dataset)</div>
+    <img src="screens/myplot1.png" width="650" title="датасет 1">
+</p>
+<p>
+    <div>Графики регрессии</div>
+    <img src="screens/myplot2.png" width="450" title="линейная модель">
+    <img src="screens/myplot3.png" width="450" title="полиномиальная модель">
+    <img src="screens/myplot4.png" width="450" title="гребневая модель">
+    <div>
+        Линейная MSE: 0.0936
+        Полиномиальная (degree=3) MSE: 0.0674
+        Гребневая (degree=3, alpha=1.0) MSE: 0.0682
+    </div>
+</p>
+
+<p>
+    <div>Набор кругов (circles_dataset)</div>
+    <img src="screens/myplot5.png" width="650" title="датасет 2">
+</p>
+<p>
+  <div>Графики регрессии</div>
+    <img src="screens/myplot6.png" width="450" title="линейная модель">
+    <img src="screens/myplot7.png" width="450" title="полиномиальная модель">
+    <img src="screens/myplot8.png" width="450" title="гребневая модель">
+    <div>
+        Линейная MSE: 0.2684
+        Полиномиальная (degree=3) MSE: 0.1341
+        Гребневая (degree=3, alpha=1.0) MSE: 0.1312
+    </div>
+</p>
+
+<p>
+    <div>Набор линейный (linearly_dataset)</div>
+    <img src="screens/myplot9.png" width="650" title="датасет 3">
+</p>
+<p>
+    <div>Графики регрессии</div>
+    <img src="screens/myplot10.png" width="450" title="линейная модель">
+    <img src="screens/myplot11.png" width="450" title="полиномиальная модель">
+    <img src="screens/myplot12.png" width="450" title="гребневая модель">
+    <div>
+        Линейная MSE: 0.1101
+        Полиномиальная (degree=3) MSE: 0.1045
+        Гребневая (degree=3, alpha=1.0) MSE: 0.1078
+    </div>
+</p>
+
+<div>
+Итоговая модель подбирается учитывая зависимость в данных,
+как правило полиномиальная регрессия справляется лучше, а коэф регуляризации в гребневой регрессии помогает избежать
+переобучения.
+</div>

antonov_dmitry_lab_1/lab1.py

@@ -0,0 +1,97 @@
+import numpy as np
+from matplotlib import pyplot as plt
+from skimage.metrics import mean_squared_error
+from sklearn.datasets import make_moons, make_circles, make_classification
+from sklearn.linear_model import LinearRegression, Ridge
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler, PolynomialFeatures
+
+X, y = make_classification(
+    n_features=2,
+    n_redundant=0,
+    n_informative=2,
+    random_state=0,
+    n_clusters_per_class=1
+)
+
+rng = np.random.RandomState(2)
+X += 2 * rng.uniform(size=X.shape)
+linearly_dataset = (X, y)
+moon_dataset = make_moons(noise=0.3, random_state=0)
+circles_dataset = make_circles(noise=0.2, factor=0.5, random_state=1)
+datasets = [moon_dataset, circles_dataset, linearly_dataset]
+
+"""
+Данные:
+· moon_dataset
+· circles_dataset
+· linearly_dataset
+"""
+for ds_cnt, ds in enumerate(datasets):
+    X, y = ds
+    X = StandardScaler().fit_transform(X)
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=.4, random_state=42
+    )
+    """
+    Модели:
+    · Линейную регрессию
+    · Полиномиальную регрессию (со степенью 3)
+    · Гребневую полиномиальную регрессию (со степенью 3, alpha = 1.0)
+    """
+
+    # Линейная
+    linear_regression = LinearRegression()
+    linear_regression.fit(X_train, y_train)
+    linear_predictions = linear_regression.predict(X_test)
+    linear_mse = mean_squared_error(y_test, linear_predictions)
+
+    # Полиномиальная (degree=3)
+    poly_regression = make_pipeline(PolynomialFeatures(degree=3), LinearRegression())
+    poly_regression.fit(X_train, y_train)
+    poly_predictions = poly_regression.predict(X_test)
+    poly_mse = mean_squared_error(y_test, poly_predictions)
+
+    # Гребневая (degree=3, alpha=1.0)
+    poly_regression_alpha = make_pipeline(PolynomialFeatures(degree=3), Ridge(alpha=1.0))
+    poly_regression_alpha.fit(X_train, y_train)
+    poly_alpha_predictions = poly_regression_alpha.predict(X_test)
+    poly_alpha_mse = mean_squared_error(y_test, poly_alpha_predictions)
+
+    # График данных
+    plt.figure(figsize=(10, 6))
+    plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap='coolwarm')
+    plt.title('Датасет №' + str(ds_cnt))
+    plt.xlabel('X')
+    plt.ylabel('Y')
+
+    # График линейной модели
+    plt.figure(figsize=(10, 6))
+    plt.scatter(X_test[:, 0], X_test[:, 1], c=linear_predictions, cmap='coolwarm')
+    plt.title('Линейная ds'+ str(ds_cnt))
+    plt.xlabel('X')
+    plt.ylabel('Y')
+    plt.show()
+
+    # График полиномиальной модели (degree=3)
+    plt.figure(figsize=(10, 6))
+    plt.scatter(X_test[:, 0], X_test[:, 1], c=poly_predictions, cmap='coolwarm')
+    plt.title('Полиномиальная (degree=3) ds' + str(ds_cnt))
+    plt.xlabel('X')
+    plt.ylabel('Y')
+    plt.show()
+
+    # График гребневой модели (degree=3, alpha=1.0)
+    plt.figure(figsize=(10, 6))
+    plt.scatter(X_test[:, 0], X_test[:, 1], c=poly_alpha_predictions, cmap='coolwarm')
+    plt.title('Гребневая (degree=3, alpha=1.0) ds' + str(ds_cnt))
+    plt.xlabel('X')
+    plt.ylabel('Y')
+    plt.show()
+
+    # Сравнение качества
+    print('Линейная MSE:', linear_mse)
+    print('Полиномиальная (degree=3) MSE:', poly_mse)
+    print('Гребневая (degree=3, alpha=1.0) MSE:', poly_alpha_mse)
+

antonov_dmitry_lab_2/README.md

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

antonov_dmitry_lab_2/lab2.py

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

antonov_dmitry_lab_3/README.md

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

antonov_dmitry_lab_3/lab3.py

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

antonov_dmitry_lab_3/titanic.py

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

antonov_dmitry_lab_4/README.md

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

antonov_dmitry_lab_4/lab4.py

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

antonov_dmitry_lab_5/README.md

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