Bank Customer Churn Predictor v2

Question

Overview
The Bank Customer Churn Predictor is a full-stack machine learning application designed to help businesses and banks identify at-risk customers, improve retention strategies, and track customer behavior patterns.

Core Highlights

• Supports multiple machine learning models including Random Forest and
  SVM

  Integrated Supabase database for backend persistence and user state
  management

  EmailJS used to send automated alters upon authentication

  Modular and page-wise routing with a clean, responsive UI

  Secure login and signup with session-aware dashboards

  Visualized churn predictions, feature importance, and customer
  metrics

  Fully responsive with mobile-first routing and pop-up page guidance

Demo Video:
https://youtu.be/zk4e3VFzg0E

How to Get Involved
The project is open-source and hosted on GitHub, but access requires sponsorship.
If you're interested in exploring the code, contributing to new features, or collaborating privately, please refer to the GitHub Sponsors link for access. I'm open to meaningful contributions, suggestions, and feedback from the community.

Comments

Thanks for sharing this comprehensive churn predictor project! It’s great to see ML models integrated with a clean full-stack setup. How do you handle model updates and retraining as new customer data comes in?

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Thanks, have thought of wiring up a siamese network (lightweight) which could auto verify the borderline cases without any latency issues, to skip the manual review involved in implementing a confidence threshold tuning or a secondary verification layer to maintain precision without impacting real-time performance..

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Supabase

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: through with updated settings‼️