38 lines
1.3 KiB
Python
38 lines
1.3 KiB
Python
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import pandas as pd
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import mean_squared_error, r2_score
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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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# Загрузка данных
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data = pd.read_csv("smoking_drinking_dataset.csv")
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# # Подготовка данных
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data = pd.get_dummies(data, columns=['sex', 'DRK_YN'], drop_first=True)
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# Разделение данных на признаки (X) и целевую переменную (y)
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X = data.drop(columns=['SMK_stat_type_cd'])
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y = data['SMK_stat_type_cd']
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# Разделение данных
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# Построение полиномиальных признаков
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poly = PolynomialFeatures(degree=2)
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X_train_poly = poly.fit_transform(X_train)
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X_test_poly = poly.transform(X_test)
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# Обучение модели
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model = LinearRegression()
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model.fit(X_train_poly, y_train)
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# Предсказание на тестовых данных
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y_pred = model.predict(X_test_poly)
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# Оценка модели
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mse = mean_squared_error(y_test, y_pred)
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r2 = r2_score(y_test, y_pred)
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# Вывод результатов
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print(f"Mean Squared Error: {mse}")
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print(f"R^2 Score: {r2}")
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