Alumni Management System - Server

This repository contains the server-side code for the Alumni Management System, including Flask API and machine learning models for career path prediction and employment probability post-graduation.

Features

• RESTful API: Built with Flask for alumni data management
• Machine Learning Models:
  • Career Path Prediction based on degree and skills
  • Employment Probability Post-Graduation prediction
• MongoDB Integration: Secure storage of alumni data and ML models
• JWT Authentication: Secure API access

Tech Stack

• Backend: Flask (Python)
• Database: MongoDB
• ML: Scikit-learn, XGBoost, Random Forest
• Authentication: JWT

Project Structure

server/
├── src/                 # Source code
│   ├── models/          # Data models
│   ├── routes/          # API routes
│   └── utils/           # Utility functions
├── combined_app.py      # Combined Flask application
├── init_db.py           # Database initialization
├── run.py               # Server entry point
└── requirements.txt     # Python dependencies

API Endpoints

• /api/admin/login - Admin login
• /api/admin/dashboard - Dashboard data
• /api/admin/profile - Admin profile
• /api/prediction/models - Get all prediction models
• /api/prediction/employment-probability - Predict employment probability
• /api/prediction/career-path-prediction - Predict career path

Getting Started

Prerequisites

• Python 3.9+
• MongoDB Atlas account (or local MongoDB instance)
• Git

Installation

1. Clone the repository:

   git clone https://github.com/mhpen/alumni-server.git
   cd alumni-server
   

2. Set up environment variables:

   • Create a .env file with the following variables:

     MONGODB_URI=mongodb+srv://dsilva:7DaXRzRoueTBa3a5@alumnimanagement.f10hpn9.mongodb.net/?retryWrites=true&w=majority&appName=AlumniManagement
     DATABASE_NAME=alumni_management
     JWT_SECRET_KEY=your_super_secret_key_for_jwt_tokens
     PORT=5000
     

3. Install dependencies:

   pip install -r requirements.txt
   

4. Initialize the database:

   python init_db.py
   

5. Start the server:

   python run.py
   

Deployment

This server can be deployed to various platforms:

1. Render.com:

   • Create a new Web Service
   • Connect your GitHub repository
   • Set the build command: pip install -r requirements.txt
   • Set the start command: gunicorn combined_app:combined_app
   • Add the environment variables from your .env file

2. Heroku:

   • Create a new app
   • Connect your GitHub repository
   • Add the Python buildpack
   • Set the environment variables
   • Deploy the app

Machine Learning Models

The system includes two ML models:

1. Career Path Prediction:

   • Uses Random Forest, XGBoost, and Logistic Regression
   • Predicts potential career paths based on degree and skills
   • Accuracy: 92.7%

2. Employment Probability Post-Graduation:

   • Uses Random Forest Regressor, XGBoost Regressor, and Linear Regression
   • Predicts the likelihood of employment after graduation
   • Accuracy: 95.2%

License

This project is licensed under the MIT License.

Acknowledgments

• Developed by Augment Agent
• Powered by Alumni Management System