24 lines
789 B
Python
24 lines
789 B
Python
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import mlflow.keras
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import pandas as pd
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import numpy as np
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from sklearn.preprocessing import MinMaxScaler
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model = mlflow.keras.load_model("model")
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test_data = pd.read_csv('./data/car_prices_test.csv')
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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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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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