Design a data analytics system that predicts customer churn based on transactional and behavioral data. The project will assist businesses in identifying at-risk customers and developing targeted retention strategies.
Collect customer transaction and engagement datasets.
Perform data preprocessing and feature engineering.
Apply classification algorithms such as Logistic Regression or Random Forest.
Evaluate model performance using confusion matrix and accuracy scores.
Develop dashboards showing churn probability and customer segments.
Suggest personalized retention strategies based on analytics results.
Document model comparison, dataset description, and strategic insights.
