39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
import pandas as pd
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import math
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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from sklearn.linear_model import LinearRegression
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from sklearn.ensemble import RandomForestRegressor
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from sklearn import metrics
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df = pd.read_csv('data.csv')
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scaler = StandardScaler()
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X = scaler.fit_transform(df.iloc[:, :-1])
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y = df.iloc[:, -1]
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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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print("Podzielono zbiór na {} rekordów uczących i {} rekordów testowych".format(len(y_train), len(y_test)))
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regressor_lin = LinearRegression()
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regressor_lin.fit(X_train, y_train)
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print("\nWyuczono model regresjii liniowej:")
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predicted_prices_lin = regressor_lin.predict(X_test)
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rmse_lin = math.sqrt(metrics.mean_squared_error(y_test, predicted_prices_lin))
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mae_lin = metrics.mean_absolute_error(y_test, predicted_prices_lin)
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print('RMSE: {:.2f} zł'.format(rmse_lin))
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print('MAE: {:.2f} zł'.format(mae_lin))
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regressor_RF = RandomForestRegressor(random_state=8)
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regressor_RF.fit(X_train, y_train)
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print("\nWyuczono model regresji drzew Random Forest:")
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predicted_prices_RF = regressor_RF.predict(X_test)
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rmse_RF = math.sqrt(metrics.mean_squared_error(y_test, predicted_prices_RF))
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mae_RF = metrics.mean_absolute_error(y_test, predicted_prices_RF)
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print('RMSE: {:.2f} zł'.format(rmse_RF))
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print('MAE: {:.2f} zł'.format(mae_RF))
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