# Saving model to disk
#ensemble_model.save_model('model.json')
#dump model using joblib
#joblib_file = "joblib_ML_Model2.json"
#joblib.dump(gb_model, joblib_file)
#load ml model using joblib
#joblib_ML_model = joblib.load(joblib_file)
#output model parameters
#joblib_ML_model
# Saving model to disk
pickle.dump(ensemble_model, open('model.pkl', 'wb'))
ensemble_model.dump_model('xgb_model.pkl')
pickle.dump(rf_model, open('model2.pkl', 'wb'))
pickle.dump(gb_model, open('model3.pkl', 'wb'))
pickle.dump(ada_model, open('model4.pkl', 'wb'))
pickle.dump(knn_model, open('model5.pkl', 'wb'))
pickle.dump(ann_model, open('model6.pkl', 'wb'))
# Loading model to compare the results
#model = pickle.load(open('model3.pkl','rb'))
#load saved model
#xgb2 = joblib.load(joblib_file)
# Loading model to compare the results
#model = open('model.json','rb')
model = pickle.load(open('model6.pkl', 'rb'))
