model_lgb.fit(train, y_train) # Fitting the train values on the model
lgb_train_pred = model_lgb.predict(train) # Train predictions
lgb_pred=model_lgb.predict_proba(test) # Predicting on test set and storing them
lgb_model = "lgb.pkl"
with open(lgb_model, 'wb') as file: # Saving the weights file
pickle.dump(model_lgb, file)
GBoost.fit(train, y_train) # Fitting the train values on the model
GBoost_train_pred = GBoost.predict(train) # Train predictions
GBoost_pred = GBoost.predict_proba(test.values) # Predicting on test set and storing them
Gboost_model= "Gboost.pkl"
with open(Gboost_model, 'wb') as file: # Saving the weights file
pickle.dump(GBoost, file)
rf_random.best_estimator_.fit(train, y_train) # Fitting the train values on the model
rf_random.best_estimator_train_pred = rf_random.best_estimator_.predict(train) # Train predictions
rf_random.best_estimator_pred = rf_random.best_estimator_.predict_proba(test.values) # Predicting on test set and storing them
rf_model= "rf.pkl"
with open(rf_model, 'wb') as file: # Saving the weights file
pickle.dump(rf_random.best_estimator_, file)
"""## Loading Weights"""
Gboost= "./Gboost.pkl" # Loading weights from weights file
with open(Gboost, 'rb') as file:
Gboost_model = pickle.load(file)
GBoost_pred = Gboost_model.predict_proba(test.values) # Finding the prediction probabilities for test values
lgb= "./lgb.pkl" # Loading weights from weights file
with open(lgb, 'rb') as file:
