CampusWallet

Student Finance Intelligence Platform

CampusWallet is a student finance intelligence platform built using a microservices architecture. It leverages the PERN stack (PostgreSQL, Express, React, Node.js) for the core web application and a Python-based FastAPI microservice for machine learning functionalities like transaction classification, anomaly detection, and forecasting.

Architecture

• Frontend: React.js dashboard for transaction input and insights visualization.
• Backend API: Node.js/Express server handling authentication, validation, database interactions, and routing requests to the ML service.
• ML Microservice: Python/FastAPI service responsible for ML predictions (classification, anomaly detection, forecasting).
• Database: PostgreSQL storing users, transactions, and cohort benchmark data.

Features

1. Transaction Classification: Automatically categorizes transactions as "wants" or "needs" using a Logistic Regression model trained on descriptions.
2. Anomaly Detection: Flags unusual spending patterns using an Isolation Forest model to detect outliers based on historical category spending.
3. Forecasting: Predicts next month's spending trends using time-series forecasting.
4. Peer Benchmarks: Compares individual spending against cohort data to provide actionable insights.

Prerequisites

• Node.js (v18+)
• Python (3.8+)
• PostgreSQL
• npm or yarn

Installation & Local Setup

1. Database Setup

Ensure PostgreSQL is running. Create a database for the project (e.g., campuswallet) and run the initial migrations/schema setup.

2. Backend (Node.js/Express)

cd backend
npm install
# Set up your .env file with DATABASE_URL and PORT (e.g., 5000)
npm start

3. ML Service (Python/FastAPI)

cd ml-service
python -m venv venv
# Activate virtual environment
# Windows: venv\Scripts\activate
# Mac/Linux: source venv/bin/activate
pip install -r requirements.txt
uvicorn main:app --reload --port 8000

4. Frontend (React)

cd frontend
npm install
# Set up your .env file with backend API URLs
npm run dev

Running the Application

Once all three services (Backend on port 5000, ML Service on port 8000, and Frontend on port 3000/5173) are running, navigate to the frontend URL in your browser to access the CampusWallet dashboard.