def accuracies(): data = dataProcessing() accuracies = [] X_train, X_test, y_train, y_test = train_test_split(data[0], data[1], test_size=0.2, random_state=1234) # all model accuracies except neural network for i in range(1, 5): model = models.classifierModel(X_train, y_train, i) accuracies.append( round(models.modelAccuracies(y_test, model, X_test), 2)) # neural network accuracy model = models.classifierModel(X_train, y_train, 5) accuracies.append(round(model[0].history['accuracy'][0] * 100, 2)) return {'accuracies': accuracies}
def randomForest(): data = dataProcessing() X_train, X_test, y_train, y_test = train_test_split(data[0], data[1], test_size=0.2, random_state=1234) model = models.classifierModel(X_train, y_train, 3) res = request.get_json() results = dataPrediction(res, model) return {'result': str(results)}
def neuralNetwork(): data = dataProcessing() X_train, X_test, y_train, y_test = train_test_split(data[0], data[1], test_size=0.2, random_state=1234) model = models.classifierModel(X_train, y_train, 5) res = request.get_json() input_pred = model[1].predict([res]) print(input_pred) return {'result': str(input_pred)}