SaveEazy

• 

  Transaction page

• 

  Budget page

• 

  Analysis page

• 

  Main page

Inspiration

I’ve always struggled with tracking my spending—random subscriptions, impulsive purchases, and forgotten expenses made budgeting feel overwhelming. Existing apps either had too many features (making them complex) or too few (lacking insights). I wanted a simple, intuitive tool that could: 1)Automatically categorize expenses 2) Highlight wasteful spending 3) Encourage smarter saving habits

What it does

1)SaveEazy is a personal finance analyzer that helps users manage their money smartly. It allows users to: Add, view, and delete transactions (income or expenses). Set monthly budgets for different spending categories like Food, Rent, Transport, etc. Track how much they’ve spent and how much remains in each category. Visualize spending patterns through interactive charts (line and pie charts). 2)Get an overview of: Total Budget Total Spent Remaining Budget 3)Budget Usage % The tool integrates a Flask backend API (deployed on Render) with a Streamlit frontend (deployed on Streamlit Cloud) to provide a seamless and real-time finance tracking experience.

How we built it

Backend: Python Flask (Pandas, NumPy) Frontend: Streamlit (for a no-fuss web UI) Visualization: Plotly Express, Matplotlib Deployment: Streamlit Sharing (for quick sharing)

Challenges we ran into

1) Data Formatting Issues – Bank exports came in different formats (CSV, Excel), requiring custom parsers. 2) Streamlit Performance – Large datasets slowed rendering; fixed with data chunking. 3) Balancing Simplicity & Depth – Avoiding feature bloat while keeping insights meaningful

Accomplishments that we're proud of

1)Successfully integrated a full-stack personal finance analyzer with a Flask backend and a Streamlit frontend. 2)Hosted the backend on Render and frontend on Streamlit Cloud, making the project publicly accessible. 3)Implemented real-time budgeting, transaction tracking, and spending analysis features. 4)Designed intuitive UI/UX that makes financial insights easy to understand for users. 5)Overcame deployment challenges related to API endpoint structuring and CORS issues.

What we learned

1)How to build and connect RESTful APIs using Flask and use them from a frontend client (Streamlit). 2)Hosting and deployment practices using Render and Streamlit Cloud. 3)Handling and debugging API endpoint issues (e.g., 404, CORS) effectively. 4)Best practices for clean code architecture, separating concerns across backend and frontend layers. 5)How to create meaningful data visualizations with Altair to display insights from financial data. 6)Gained practical experience with project version control on GitHub and iterative debugging.

What's next for SaveEazy

Bank API integration (Plaid/Yodlee) for real-time sync. AI-Powered Suggestions ("You spend 40% more on dining this month!"). Mobile App (Kivy/Flutter) for on-the-go tracking.

Built With

• flask
• matplotlib
• numpy
• pandas
• python
• sql
• sqlite
• streamlit