Course Recommendation Model

This repository contains the source code for a Course Recommendation System. The project includes a backend implementation for course recommendations and a front-end interface for user interaction. The system guides users in choosing courses based on their preferences and inputs.

Features

• Interactive Chat Interface: A user-friendly web interface for interacting with the recommendation system.
• Dynamic Recommendations: Personalized course suggestions with reasons, powered by the backend.
• Easy Deployment: Designed to be deployed locally or on a web server with minimal setup.

Project Structure

• backend.py: Handles the server-side logic, including:

  • Processing user inputs.
  • Generating course recommendations.
  • Managing the flow of questions and responses.

• index-2.html: Implements the front-end interface for the chat-based interaction, featuring:

  • Real-time communication with the backend.
  • User-friendly chat layout and design.
  • Dynamic message handling for recommendations and feedback.

Getting Started

Prerequisites

• Python 3.8+
• Flask (or any other Python-based web framework)
• Basic knowledge of HTML, CSS, and JavaScript.

Installation

1. Clone the repository:

   git clone https://github.com/nasal-thanseer/course-recommendation-model.git
   cd course-recommendation-model

2. Set up the backend:

   • Install Python dependencies:

     pip install flask

   • Run the server:

     python backend.py

3. Set up the front-end:

   • Open index-2.html in any browser or serve it using a simple HTTP server:

     python -m http.server 8080

4. Access the system:

   • Navigate to http://localhost:8080 in your browser.

Usage

1. Open the front-end page (index-2.html) in your browser.
2. Start interacting with the chat interface.
3. Follow the questions and get personalized course recommendations.

File Details

Backend

• backend.py
  • Listens for user inputs and processes responses.
  • Generates course recommendations dynamically.
  • Endpoints:
    • /recommend - Processes user inputs and returns recommendations.
    • /questions - Fetches the next question.

Frontend

• index-2.html
  • Styled for a split-screen layout with an interactive chat box.
  • Uses JavaScript to handle user inputs and backend responses dynamically.

Future Enhancements

• Add a database for storing user preferences and course data.
• Integrate AI/ML models for more accurate recommendations.
• Deploy on a cloud platform like AWS or Heroku.

Contributing

We welcome contributions! Please fork the repository, make changes, and submit a pull request.