2024-04-28 20:29:40 +02:00
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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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import tensorflow as tf
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from sklearn.preprocessing import MinMaxScaler
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2024-05-13 21:05:29 +02:00
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test_data = pd.read_csv('./data/car_prices_test.csv')
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2024-04-28 20:29:40 +02:00
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test_data.dropna(inplace=True)
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y_test = test_data['sellingprice'].astype(np.float32)
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X_test = test_data[['year', 'condition', 'transmission']]
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scaler_y = MinMaxScaler()
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scaler_y.fit(y_test.values.reshape(-1, 1))
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scaler_X = MinMaxScaler()
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X_test['condition'] = scaler_X.fit_transform(X_test[['condition']])
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X_test = pd.get_dummies(X_test, columns=['transmission'])
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2024-05-13 21:03:34 +02:00
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model = tf.keras.models.load_model('./car_prices_predict_model.h5')
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2024-04-28 20:29:40 +02:00
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y_pred_scaled = model.predict(X_test)
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y_pred = scaler_y.inverse_transform(y_pred_scaled)
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y_pred_df = pd.DataFrame(y_pred, columns=['PredictedSellingPrice'])
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y_pred_df.to_csv('predicted_selling_prices.csv', index=False)
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