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This is one more function- def event_prediction_tfidf(input_ticket_category, input_ticket_type, input_ticket_item, input_ticket_summary, input_ticket_desc, input_ticket_severity): #pdb.set_trace() global tfidf_matrix,tf_count_matrix,tfidf_vector,count_vector,df_act ## First join 5 parameters andd then call input_data_preprocessing data_to_be_processed=str(input_ticket_category) +' ' + str(input_ticket_type) +' ' +str(input_ticket_item) + ' ' + str(input_ticket_summary) + ' ' +str(input_ticket_desc) + ' ' + str(input_ticket_severity) ## Input Data Preprocessing input_processed_text = input_data_preprocessing(data_to_be_processed) ## 5 different fields print("Input processed Text : ",input_processed_text) #pdb.set_trace() ##TFIDF Prediction tfidf_pred,input_tfidfmatrx = input_evalution(input_processed_text,tfidf_matrix,tfidf_vector,df_act) ##TF_count Prediction tf_count_pred,input_tfcountmatrx = input_evalution_count(input_processed_text,tf_count_matrix,count_vector,df_act) #pdb.set_trace() tfidf_pred['score_new'] = tfidf_pred['score']*0.5 tf_count_pred['score_new'] = tf_count_pred['score']*0.5 tfidf_pred['flag'] = 'tfidf' tf_count_pred['flag'] = 'tf_count' overall_result = pd.concat([tfidf_pred,tf_count_pred]) print("Overall Result : ",overall_result) if len(overall_result)>0: overall_result = overall_result.sort_values(by='score_new',ascending=False) #print it and then change it overall_result.drop_duplicates(subset = 'event_id',inplace=True) #overall_result =overall_result[overall_result['event_type']==input_event_type] overall_result['fuzz_valid_score'] = overall_result.apply(lambda row: fuzz_score(input_processed_text,row['clean_text_event_title']),axis=1) overall_result = overall_result[(overall_result['fuzz_valid_score']>config.fuzzy_threshold) |(overall_result['score_new']>=config.tf_threshold)] overall_result = overall_result.head(config.max_reccom) overall_result_1 = overall_result[overall_result['event_type']==input_event_type] overall_result_2 = overall_result[overall_result['event_type']!=input_event_type] if len(overall_result_1) < 10: overall_result = overall_result_1.append(overall_result_2, ignore_index=True) else: overall_result = overall_result_1 event_id_list = overall_result['event_id'].tolist() event_title_list = overall_result[config.target_column].tolist() return event_id_list,event_title_list ## we need only result list else: event_id_list = [] event_title_list = [] return event_id_list,event_title_list ## user recommendation list I have modified few things as per my requirement. but I am stuck here after these lines till end- overall_result.drop_duplicates(subset = 'event_id',inplace=True) #overall_result =overall_result[overall_result['event_type']==input_event_type] Like I dont have any event_id in my columns and person who resolved is target column and rest columns I am passing as input. Can you help me here with changes please.