ium_464962/model.py

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2024-04-28 20:29:40 +02:00
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')