50 lines
1.9 KiB
Python
50 lines
1.9 KiB
Python
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import Lasso
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from sklearn.metrics import mean_squared_error, r2_score
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from sklearn.preprocessing import StandardScaler
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# Загрузка данных
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data = pd.read_csv('Hostel.csv')
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# Выбор нужных признаков
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features = ['price.from', 'Distance', 'atmosphere', 'cleanliness', 'facilities']
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# Очистка данных и заполнение пропущенных значений
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data['Distance'] = data['Distance'].str.replace('km from city centre', '').astype(float)
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data['summary.score'] = data['summary.score'].fillna(data['summary.score'].mean())
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# Заполнение пропущенных значений (например, средними)
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data[features] = data[features].fillna(data[features].mean())
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# Разделение данных на обучающий и тестовый наборы
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X = data[features]
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y = data['summary.score']
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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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# Масштабирование данных (стандартизация)
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scaler = StandardScaler()
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X_train_scaled = scaler.fit_transform(X_train)
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X_test_scaled = scaler.transform(X_test)
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# Обучение модели Лассо-регрессии
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alpha = 0.1 # параметр регуляризации
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lasso = Lasso(alpha=alpha)
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lasso.fit(X_train_scaled, y_train)
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# Предсказание на тестовом наборе
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y_pred = lasso.predict(X_test_scaled)
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# Оценка качества модели
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mse = mean_squared_error(y_test, y_pred)
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accuracy = 1 - mse / y_test.var()
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# Вывод точности модели
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print(f"\nТочность модели: {accuracy * 100:.2f}%")
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# Оценка качества 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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print(f'Mean Squared Error (MSE): {mse:.4f}')
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print(f'R-squared: {r2:.4f}')
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