61 lines
2.3 KiB
Python
61 lines
2.3 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
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from sklearn.preprocessing import StandardScaler, OneHotEncoder
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from sklearn.compose import ColumnTransformer
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from sklearn.pipeline import Pipeline
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import matplotlib.pyplot as plt
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# Загрузка данных
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file_path = 'ds_salaries.csv'
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data = pd.read_csv(file_path)
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# Предварительная обработка данных
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categorical_features = ['experience_level', 'employment_type', 'company_location', 'company_size']
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numeric_features = ['work_year']
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preprocessor = ColumnTransformer(
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transformers=[
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('num', StandardScaler(), numeric_features),
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('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features)
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])
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# Выбор признаков
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features = ['work_year', 'experience_level', 'employment_type', 'company_location', 'company_size']
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X = data[features]
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y = data['salary_in_usd']
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# Разделение данных на обучающий и тестовый наборы
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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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# Создание и обучение модели с использованием предварительного обработчика данных
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alpha = 0.01
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lasso_model = Pipeline([
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('preprocessor', preprocessor),
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('lasso', Lasso(alpha=alpha))
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])
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lasso_model.fit(X_train, y_train)
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# Получение прогнозов
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y_pred = lasso_model.predict(X_test)
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# Оценка точности модели
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accuracy = lasso_model.score(X_test, y_test)
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mse = mean_squared_error(y_test, y_pred)
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print(f"R^2 Score: {accuracy:.2f}")
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print(f"Mean Squared Error: {mse:.2f}")
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# Вывод предсказанных и фактических значений
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predictions_df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})
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print(predictions_df)
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# Визуализация весов (коэффициентов) модели
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coefficients = pd.Series(lasso_model.named_steps['lasso'].coef_, index=numeric_features + list(lasso_model.named_steps['preprocessor'].transformers_[1][1].get_feature_names(categorical_features)))
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plt.figure(figsize=(10, 6))
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coefficients.sort_values().plot(kind='barh')
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plt.title('Lasso Regression Coefficients')
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plt.show()
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