🏦 LoanIQ — Full Stack ML System

React + Node.js + MongoDB + Python ML

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📁 Project Structure

loaniq/
├── backend/
│   ├── ml_service.py     ← Python Flask ML (port 5001)
│   ├── server.js         ← Node.js Express + MongoDB (port 5000)
│   └── package.json
└── frontend/
    ├── src/
    │   ├── App.jsx
    │   ├── api.js             ← All API calls
    │   ├── index.css
    │   ├── main.jsx
    │   └── pages/
    │       ├── Predict.jsx    ← Loan form + live result
    │       ├── Dashboard.jsx  ← Charts + ML + MongoDB stats
    │       └── History.jsx    ← All past predictions (CRUD)
    ├── index.html
    ├── package.json
    └── vite.config.js

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🚀 Setup — 5 Steps

Step 1 — Install Python dependencies

pip install flask flask-cors pandas numpy scikit-learn

Step 2 — Start Python ML service

cd backend
python3 ml_service.py
# Runs on http://localhost:5001
# Trains model automatically on first run

Step 3 — Start MongoDB

# Make sure MongoDB is installed and running:
mongod --dbpath /data/db
# OR if using brew:
brew services start mongodb-community

Step 4 — Start Node.js backend

cd backend
npm install
node server.js
# Runs on http://localhost:5000

Step 5 — Start React frontend (new terminal)

cd frontend
npm install
npm run dev
# Open http://localhost:3000

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🔌 API Reference

Method	Endpoint	Description
GET	/api/health	Check all services
GET	/api/stats	ML model metrics
GET	/api/db-stats	MongoDB summary
POST	/api/predict	Run prediction + save to DB
GET	/api/predictions	All past predictions
DELETE	/api/predictions/:id	Delete a record
POST	/api/retrain	Retrain the ML model

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📄 Pages

Page	Route	Features
Predict	/	Form, instant result, probability bars, top factors
Dashboard	/dashboard	8 stat cards, 6 charts, confusion matrix
History	/history	Table, filters, pagination, expand, delete

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🛠 Tech Stack

Layer	Technology
Frontend	React 18, Vite, Recharts
Backend	Node.js, Express, Mongoose
Database	MongoDB
ML	Python, Flask, scikit-learn
Algorithm	Logistic Regression