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