Project - Stock Closing Price Prediction using Deep Learning, TensorFlow2 & Keras

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Evaluating the Model Performance

Let us now compute the model score on train data, validation data, and test data.

We shall use the model.evaluate() and print the Mean Squared Error and Root Mean Squared Error for each of the train, validation and test sets.

  • Use the below function to compute the model performance.

    import math
    def model_score(model, X_train, y_train, X_val, y_val , X_test, y_test):
        print('Train Score:')
        train_score = model.evaluate(X_train, y_train, verbose=0)
        print("MSE: {:.5f} , RMSE: {:.2f}".format(train_score[0], math.sqrt(train_score[0])))
        print('Validation Score:')
        val_score = model.evaluate(X_val, y_val, verbose=0)
        print("MSE: {:.5f} , RMSE: {:.2f}".format (val_score[0], math.sqrt(val_score[0])))
        print('Test Score:')
        test_score = model.evaluate(X_test, y_test, verbose=0)
        print("MSE: {:.5f} , RMSE: {:.2f}".format (test_score[0], math.sqrt(test_score[0])))
    model_score(model, trainX, trainY ,valX, valY , testX, testY)
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