AI-Powered Course Recommendation System

Overview

This is a Flask-based web application that extracts LinkedIn profile data and recommends career-enhancing courses using AI. The system scrapes a LinkedIn profile, cleans the extracted text, and uses an AI model to generate personalized course recommendations.

Features

• User Authentication: Signup and login functionality with session management.
• LinkedIn Profile Scraping: Extracts and cleans data from a LinkedIn profile.
• AI-Powered Recommendations: Uses the Groq API to generate a list of courses based on the user's skills and expertise.
• Dashboard Display: Displays personalized course recommendations on the user dashboard.

Technologies Used

• Backend: Flask (Python)
• Frontend: Jinja2 (HTML, CSS)
• Database: CSV-based user storage
• API Integration: ScrapingDog API for LinkedIn scraping, Groq API for AI-generated recommendations
• Natural Language Processing: NLTK for text cleaning

Installation

Prerequisites

• Python 3.x
• Virtual environment (optional but recommended)

Steps

1. Clone the repository:

   git clone https://github.com/yourusername/yourproject.git
   cd yourproject

2. Create and activate a virtual environment:

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

3. Install dependencies:

   pip install -r requirements.txt

4. Create a .env file and add your API key:

   API_KEY=your_groq_api_key
   

5. Run the application:

   python app.py

6. Open your browser and navigate to http://127.0.0.1:5000

Usage

• Signup/Login: Users create an account and log in.
• Profile Setup: Users provide their LinkedIn profile link.
• Recommendations: The system extracts relevant skills and generates recommended courses.
• Dashboard: Users view their personalized recommendations.

API Configuration

• ScrapingDog API: Used to scrape LinkedIn profile data.
• Groq AI API: Used to generate course recommendations.

File Structure

project/
│── app.py              # Main Flask application
│── templates/          # HTML templates
│── static/             # Static files (CSS, JS)
│── users.csv           # CSV file for storing user data
│── .env                # Environment variables (API keys)
│── requirements.txt    # Python dependencies
│── README.md           # Project documentation

Demo Video

Watch the demo

Future Enhancements

• Database Integration: Replace CSV with MongoDB or PostgreSQL.
• OAuth Authentication: Allow users to log in with LinkedIn.
• Enhanced NLP: Improve text processing with more advanced AI models.
• Email Notifications: Notify users about new recommended courses.

License

This project is licensed under the MIT License.

Contact

For any queries or contributions, contact isatyamks@gmail.com or visit https://github.com/isatyamks.