Student Depression Prediction Web App

This project is a full-stack web application that predicts student depression levels using a machine learning model. It features a Next.js frontend and a Flask backend, deployed using Vercel and Railway respectively.

🚀 Features

• 🧠 Machine Learning model for depression prediction
• 🌐 Frontend built with Next.js & Tailwind CSS
• 🐍 Backend API with Flask
• 📦 RESTful API connection between frontend and backend
• ☁️ Deployed on Vercel (frontend) and Railway (backend)

🧩 Tech Stack

Frontend

• Next.js (React)
• Tailwind CSS
• TypeScript

Backend

• Flask
• Flask-CORS
• scikit-learn, pandas, joblib (for model serving)

Deployment

• Frontend: Vercel
• Backend: Railway

🛠️ Installation

1. Clone the Repository

git clone https://github.com/valentypo/machine-learning.git
cd machine-learning

2. Setup Frontend (Next.js)

cd src
npm install
# create .env.local file and add:
# NEXT_PUBLIC_API_URL=[https://your-backend-url/api](http://depression-learning-backend-production.up.railway.app)
npm run dev

3. Setup Backend (Flask)

cd server
pip install -r requirements.txt
python app.py

Make sure the backend runs on port 5000 or update the frontend's .env.local accordingly.

## 🌍 Deployment

- **Frontend deployed on Vercel:** https://machine-learning-mocha.vercel.app/
- **Backend deployed on Railway:** http://depression-learning-backend-production.up.railway.app

## ✨ Credits

Created by 
[@Cavinee & @valentypo]