38 lines
1.3 KiB
Python
38 lines
1.3 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 keras.models import Sequential
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from keras.layers import Dense
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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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model = Sequential()
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model.add(Dense(32, activation='relu', input_dim=(X_train.shape[1])))
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model.add(Dense(32, activation='relu'))
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model.add(Dense(1, activation='linear'))
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print("Stworzono sieć neuronową: \n")
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model.summary()
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model.compile(optimizer='adam', loss='mean_squared_error', metrics=["mae"])
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epochs = 1500
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model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test))
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print("Zakończono trenowanie sieci neuronowej z wykorzystaniem biblioteki Keras.")
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predicted_prices = model.predict(X_test)
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rmse = math.sqrt(metrics.mean_squared_error(y_test, predicted_prices))
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mae = metrics.mean_absolute_error(y_test, predicted_prices)
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print('RMSE: {:.2f} zł'.format(rmse))
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print('MAE: {:.2f} zł'.format(mae)) |