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def get_top_5_person_who_resolved(pred_data): distance_metric='cosine' print("pred_data: ", pred_data) row=feature_engineering(pred_data) print("row: ", row) label_enc = LabelEncoder() #row['role_name_encoded'] = label_enc.fit_transform(row['role_name']) #row['role_name_decoded'] = label_enc.inverse_transform(row['role_name_encoded']) # Link the X vector with index #index = row.index.values ##Fetching the ticket data details from API #pdb.set_trace() ticket_data= ticket_data = pd.concat(map(pd.read_csv, ['/Analytics/venv/Jup/CAPE_ServicePlus_UC/ServicePlusIncidentData_Post_01-01-2019_Till_07-07-2019.csv', '/Analytics/venv/Jup/CAPE_ServicePlus_UC/ServicePlusTicket_Data_Till-2019-01-01.csv']), ignore_index=True) df=feature_engineering(ticket_data) # Sample training data with text features train_data = df.drop(columns=['person_who_resolved',,'owner_user_id','role_name']) output_df =df[['person_who_resolved','owner_user_id','role_name']] # New data for similarity calculation new_data = row # Create TF-IDF vectorizer and fit on training data vectorizer = TfidfVectorizer() X = vectorizer.fit_transform(train_data) # Transform new data using the same vectorizer X_new = vectorizer.transform(new_data) # Calculate cosine similarity between new data and training data similarity_matrix = cosine_similarity(X_new, X) # Find the most similar training data indices for each new data point similar_indices = np.where(similarity_matrix > 0.5) print('Similar Indices',similar_indices) # Get the corresponding output TF-IDF vectors for new data predicted_output_data=output_df.iloc[similar_indices] print("Predicted Output",predicted_output_data) print("Similarity Matrix:") print(similarity_matrix) print("\nSimilar Data Indices with Cosine Similarity > 0.5:", similar_indices) print("\nPredicted Output Data:") print(predicted_output_data)