35 lines
945 B
Python
35 lines
945 B
Python
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from sklearn.metrics import mean_absolute_percentage_error
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from sklearn.model_selection import train_test_split
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from sklearn.neural_network import MLPClassifier
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import pandas as pd
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def MEDV_to_number(MEDV):
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if MEDV < (minMEDV + meanMEDV) / 2:
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return 1
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if MEDV < (maxMEDV + meanMEDV) / 2:
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return 2
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return 3
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data = pd.read_csv('boston.csv')
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minMEDV = data['MEDV'].min()
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maxMEDV = data['MEDV'].max()
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meanMEDV = data['MEDV'].mean()
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data['MEDV'] = data['MEDV'].apply(MEDV_to_number)
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X = (data[['RM', 'LSTAT', 'CRIM']])
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y = data['MEDV']
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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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clf = MLPClassifier(hidden_layer_sizes=(100, 100), activation='relu', solver='lbfgs', max_iter=1000)
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clf.fit(X_train, Y_train)
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y_predict = clf.predict(X_test)
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score = clf.score(X_test, Y_test)
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print(f'Score: {score}')
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print(f'MAPE: {mean_absolute_percentage_error(Y_test, y_predict)}')
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