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SaveDollar

SaveDollar is a web-based personal finance assistant I built to manage and analyze my own expenses more intelligently. The app integrates AI-powered forecasting with financial dashboards to help users track their income, expenses, budgets, savings goals, and spending trends with ease.

💸 SaveDollar – Intelligent Personal Finance Forecasting App

SaveDollar is a smart personal finance assistant designed to help users make sense of their spending, categorize transactions, and predict future expenses. Built as a capstone-style solo project, SaveDollar showcases the intersection of machine learning, data science, and frontend engineering. It reflects my core strength as a data-driven developer who cares about delivering user-centered insights with simplicity and automation.

“Everybody should learn to program a computer, because it teaches you how to think.” – Steve Jobs

🧠 Project Background

The idea for SaveDollar came from a real-world observation: most people, especially students and young professionals, struggle with managing their finances. While numerous apps exist, they often lack intelligent predictions and customizable insights. As a data science student passionate about AI-driven solutions, I wanted to create an app that not only tracks expenses but also learns from your past behavior to forecast your future spending — like a personal budgeting assistant that gets smarter with time.

SaveDollar was developed using a full-stack approach. The frontend was built using React, Tailwind CSS, and Shadcn/UI for a sleek and responsive interface, while the backend model was prototyped in Python using Scikit-learn to train a forecasting pipeline. The goal was to use historical transactions (imported as .json) and deliver dynamic forecasts with minimal user setup.

✨ Key Features

  • 🔄 Transaction Import: Upload your personal .json file of bank transactions.
  • 🧠 Automated Categorization: Transactions are sorted into standard categories like groceries, rent, transport, etc.
  • 📊 Interactive Dashboard: Visualize spending trends across months and categories.
  • 🔮 ML-Based Forecasting: Scikit-learn pipeline predicts your next month’s expenses by analyzing past transaction patterns.
  • 🧩 Component-Based UI: React and Shadcn were used to build reusable, responsive, and accessible components.
  • 🌙 Dark Mode Support: Optimized for late-night financial planning!

🔧 Tech Stack

Layer Technology Purpose
Frontend React + Vite + TypeScript Fast UI, modular component development
Styling Tailwind CSS + Shadcn UI Custom styling with utility classes and prebuilt components
Backend (ML) Python + Scikit-learn + Pandas Forecasting future expenses using regression
Build Tool Vite Lightning-fast development and production build
Deployment GitHub Pages / Vercel Hosting the web app
File Parsing JSON import logic in JS Enables importing user transactions

SaveDollar expects a .json file structured like this:

🧠 Why This Project Matters

Most budgeting apps stop at what you spent. SaveDollar tells you what you might spend next.

In a world where AI recommends your next song, movie, or email — why not your next financial decision?

This project reflects my commitment to applying data science in ways that are smart, ethical, and human-focused. It’s not about complexity — it’s about clarity.

🔗 Links

👨‍💻 Author

[Harsh Dwivedi]

🎓 Data Science Graduate | 🧠 Machine Learning Enthusiast | 💻 Frontend Explorer