Ganesh2409 / Course-Recommendation-System

🚀 Course Recommendation System is a machine learning-powered web application designed to recommend similar courses from Coursera's vast dataset of over 3,000 courses. Built using Python, Scikit-learn, and Streamlit, the app preprocesses course data, applies text vectorization, and leverages cosine similarity to offer personalized recommendations.

Course Recommendation System 🚀

A Dockerized machine learning-based web application that recommends similar courses from a dataset of over 3,000 courses on Coursera. The app uses text vectorization and cosine similarity to provide personalized course recommendations based on user input.

Features 🔥

• Data preprocessing and cleaning
• Vectorization of course descriptions and skills using sklearn
• Cosine similarity-based course recommendation engine
• Interactive web interface built with Streamlit
• Fully Dockerized for easy deployment

Table of Contents 📑

• Installation
• Project Structure
• Usage
• Docker
• Contributing
• Contact

Installation 🛠️

To run the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/ganesh2409/Course-Recommendation-System.git
   cd Course-Recommendation-System

2. Create and activate a virtual environment (optional but recommended):

   python -m venv env
   source env/bin/activate  # Mac/Linux
   .\env\Scripts\activate   # Windows

3. Install the required dependencies:

   pip install -r requirements.txt

Project Structure 🗂️

Course-Recommendation-System/
├── Data/
│   └── Coursera.csv                                  # Coursera dataset
├── models/
│   ├── course_list.pkl                               # Precomputed similarity matrix
│   └── courses.pkl                                   # Processed course list
├── main.py                                           # Streamlit app script
├── CourseRecommendationSystem.py                     # Data preprocessing and model training script 
├── requirements.txt                                  # Python dependencies
├── Dockerfile                                        # Docker configuration
└── README.md                                         # Project README file

Usage ⚙️

1. Run the preprocessing and model training script:

   python CourseRecommendationSystem.py

2. Run the Streamlit application:

   streamlit run main.py

3. Navigate to the local URL (http://localhost:8501) to use the web app.

Docker Hub 🐳

To directly use the project from Docker Hub

1. Pull the pre-built Docker image:

   docker pull ganeshpinnamaneni/course-recommendation-system:latest

2. Run the Docker container:

   docker run -p 8501:8501 ganeshpinnamaneni/course-recommendation-system:latest

3. Access the web app at http://localhost:8501.

Contributing 🤝

We welcome contributions to improve the Course Recommendation System. Here's how you can contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit your changes (git commit -m 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Create a Pull Request.

Contact 📧

For any questions or feedback, feel free to reach out:

• Ganesh Chowdhary P – [e-mail]
• GitHub: Ganesh Chowdhary P

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