Study Abroad Project

The "Study Abroad" project is a comprehensive platform designed to assist students in exploring international education opportunities. It integrates a backend, frontend, and machine learning (ML) components to provide a seamless and personalized user experience.

---

Project Overview

1. Backend

The backend is built using Node.js, Express, and MongoDB. It provides APIs for:

• User authentication and profile management.
• CRUD operations for university data.
• Integration with ML models for personalized recommendations.

For more details, refer to the Backend README.

---

2. Frontend

The frontend is developed using React.js with Vite as the build tool and Tailwind CSS for styling. It offers:

• A responsive and interactive user interface.
• Features like program listings, user profile management, and university recommendations.

For more details, refer to the Frontend README.

---

3. Machine Learning Integration

The ML component enhances the platform by providing:

• Data Scraper: Scrapes university data from the internet and generates JSON files for manual storage in MongoDB.
• Recommendation Model: Suggests universities based on user preferences (e.g., degree, course, country, budget).
• Heatmap Visualization: Displays insights about student data using clustering and geospatial analysis.

ML Technologies Used:

• Python: For building and running ML components.
• BeautifulSoup & Requests: For web scraping.
• Pandas & NumPy: For data preprocessing and analysis.
• Matplotlib, Seaborn & Folium: For data visualization.
• Flask: For exposing ML models as APIs.

---

Integration Workflow

1. Frontend and Backend

• The frontend communicates with the backend using REST APIs.
• The backend handles user authentication, profile updates, and university data management.

2. Backend and ML

• The backend interacts with the ML components via Flask APIs.
• Example ML API endpoints:
  • /recommend: Accepts user preferences and returns recommended universities.
  • /heatmap: Displays a heatmap of student data.

3. Frontend and ML

• The frontend indirectly interacts with ML components through the backend.
• Example workflow:
  • The user submits preferences on the frontend.
  • The backend forwards the request to the ML API.
  • The ML API processes the data and returns results to the backend, which are then displayed on the frontend.

---

Installation and Setup

Prerequisites

• Node.js (v16 or higher)
• Python (v3.8 or higher)
• MongoDB (local or remote instance)

Steps to Run the Project

1. Clone the Repository:

   git clone <repository-url>
   cd Project-Study_Abroad

2. Set Up the Backend:

   • Navigate to the backend directory:

     cd Abroad-backend

   • Install dependencies:

     npm install

   • Create a .env file and configure environment variables:

     MONGO_URI=mongodb://0.0.0.0/Abroad
     JWT_SECRET=MY_Key

   • Start the backend server:

     npm start

3. Set Up the Frontend:

   • Navigate to the frontend directory:

     cd ../Abroad-frontend

   • Install dependencies:

     npm install

   • Create a .env file and configure the API URL:

     VITE_API_URL=http://localhost:5000/api

   • Start the development server:

     npm run dev

4. Set Up the ML Component:

   • Navigate to the ML directory:

     cd ../Abroad-ml

   • Create a virtual environment and activate it:

     python -m venv venv
     source venv/bin/activate  # On Windows: venv\Scripts\activate

   • Install dependencies:

     pip install -r requirements.txt

   • Start the Flask server:

     python app.py

---

Project Structure

Project-Study_Abroad/
├── Abroad-backend/          # Backend codebase
├── Abroad-frontend/         # Frontend codebase
├── Abroad-ml/               # Machine learning models and APIs
├── README.md                # Root README for the project

---

Key Features

1. User Authentication:

   • Secure login and registration using JWT.
   • Profile management with wishlist functionality.

2. University Data Management:

   • Add, update, and retrieve university details.

3. Personalized Recommendations:

   • Suggests universities based on user preferences using ML models.

4. Interactive Visualizations:

   • Heatmaps and insights about student data.

5. Data Scraping:

   • Scrapes university details from the internet for manual storage in MongoDB.

---

Running the Complete Project

1. Ensure all components (backend, frontend, ML) are running.
2. Access the frontend at:

   http://localhost:5173
   

3. Use the platform to explore programs, manage profiles, and get recommendations.

---

Future Enhancements

• Add more advanced ML models for better recommendations.
• Integrate payment gateways for application fee processing.
• Expand the database with more universities and programs.

---