abanin_daniil_lab_5/README.md

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

abanin_daniil_lab_5/lab5.py

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

abanin_daniil_lab_5/loan.csv

@@ -0,0 +1,615 @@
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