25/05/2023
How you can leverage text mining techniques to rank and prioritize assessments provided by banks on suspicious customers?
Data Preprocessing:
Collect the assessment data provided by banks with relevant information. Clean the data.
Feature Extraction:
Identify relevant features that can contribute to assessing the suspicious nature of customers.
Training Data:
Create a labeled dataset by associating assessments with corresponding labels (e.g., "suspicious" or "not suspicious").
Text Classification Models:
Train a text classification model on the labeled dataset.
Model Evaluation:
Evaluate the trained model's performance using appropriate metrics such as accuracy, precision, recall, or F1-score.
Ranking and Prioritization:
Apply the trained model to the new, unseen assessments provided by banks.
Ongoing Monitoring and Iteration:
Regularly update and retrain the model with new labeled data to improve its performance and adapt to evolving patterns of suspicious behavior.
Register at www.amlhackathon.com
