cols_list.append(cols)
data.scale_data(cols_list[:-1])
cols_list = data.get_cols()
y_column_name = input("enter y column name: ")
X_train, X_test, y_train, y_test = data.spilt_data(y_column_name)
model_type = input("Enter R for Regression and C for Classification: ")
if model_type == "C":
print("Your options are: " + str(Classifier_list)) #add mode list
modelname = input("Enter model to be used: ")
classifier = Classification(X_train, X_test, y_train, y_test, modelname)
classifier.predict()
classifier.accuracy()
classifier.save_model()
elif model_type == 'R':
print("Your options are: " + str(Regressor_list)) #add mode list
modelname = input("Enter model to be used, use A for all")
if modelname == "A":
for modelname in Regressor_list:
regressor = Regression(X_train, X_test, y_train, y_test, modelname)
regressor.predict()
regressor.accuracy()
regressor.save_model()
else:
regressor = Regression(X_train, X_test, y_train, y_test, modelname)
regressor.predict()
regressor.accuracy()
regressor.save_model()
