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