AI Finance Dashboard
An intelligent dashboard for personal finance management, featuring AI-powered spending insights and predictions.
Overview
A full-stack application that helps users track their expenses, categorize transactions automatically using machine learning, and predict future spending habits to help them save better.
Challenges
- 1
Building a responsive and intuitive data visualization interface handling large datasets in the browser.
- 2
Integrating a Python machine learning service with a Node/Next.js frontend efficiently.
- 3
Ensuring high performance for complex data aggregations on the database side.
Architecture & Strategy
Next.js handles the frontend and BFF (Backend-for-Frontend) API routes. A separate Python FastAPI service runs the ML models for categorization and forecasting, communicating via REST.
Results
Achieved >95% accuracy in automatic transaction categorization.
Processed over 100k transactions per user without UI lag.
Received positive feedback from initial beta testers regarding ease of use.
Lessons Learned
- Separating heavy computational tasks from the main API improves user experience.
- Client-side caching is crucial for interactive data visualization.
- Prioritizing UX in data-heavy apps makes complex information accessible.