#splitting data for validation set X_train, X_val, y_train, y_val = dp.validation_split(train_X, train_y) #scaling train and validation data X_train, X_val = dp.scale_data(X_train, X_val) #model is defined in the ModelBuilder class. mb = ModelBuilder() classifier = mb.get_classifier() #cross-validation on smaller set of training data mb.validate(classifier, X_train, y_train) ##Cross Validation - Accuracy : 98.11% (1.13%) #evaluation model using validation set mb.evaluate(classifier, X_train, y_train, X_val, y_val) ##Accuracy is 99.073% #scaling train and test data train_X, test_X = dp.scale_data(train_X, test_X) #cross-validation on complete train data mb.validate(classifier, train_X, train_y) ##Cross Validation - Accuracy : 98.57% (0.41%) #Train with complete train data classifier.fit(train_X, train_y, batch_size=10, epochs=100) #predicting on test data mb.check_prediction(classifier, test_X, test_y) #Test Data - Accuracy is 98.279%
