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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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