34 lines
889 B
Python
34 lines
889 B
Python
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import pandas as pd
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import numpy as np
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from tensorflow.keras import Sequential
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from tensorflow.keras.layers import Dense
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from sklearn.preprocessing import MinMaxScaler
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train_data = pd.read_csv('./data/car_prices_train.csv')
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train_data.dropna(inplace=True)
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y_train = train_data['sellingprice'].astype(np.float32)
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X_train = train_data[['year', 'condition', 'transmission']]
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scaler_x = MinMaxScaler()
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X_train['condition'] = scaler_x.fit_transform(X_train[['condition']])
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scaler_y = MinMaxScaler()
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y_train = scaler_y.fit_transform(y_train.values.reshape(-1, 1))
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X_train = pd.get_dummies(X_train, columns=['transmission'])
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model = Sequential([
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Dense(64, activation='relu'),
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Dense(32, activation='relu'),
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Dense(1)
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])
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model.compile(optimizer='adam', loss='mean_squared_error')
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model.fit(X_train, y_train, epochs=20, batch_size=32)
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model.save('car_prices_predict_model.h5')
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