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https://github.com/muhammadadilnaeem/course-recommender-app

This is a Streamlit application that provides personalized course recommendations based on user preferences. The app allows users to select courses they have audited or completed, trains a recommendation model, and generates recommendations for new courses.
https://github.com/muhammadadilnaeem/course-recommender-app

coursera-machine-learning pandas python streamlit streamlit-webapp

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This is a Streamlit application that provides personalized course recommendations based on user preferences. The app allows users to select courses they have audited or completed, trains a recommendation model, and generates recommendations for new courses.

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# **Course Recommender App**

This is a Streamlit application that provides personalized course recommendations based on user preferences. The app allows users to select courses they have audited or completed, trains a recommendation model, and generates recommendations for new courses.

https://github.com/user-attachments/assets/63d0e067-6355-46d1-9cbf-a97fc055eb19

## **Features**

- Interactive course selection using a dynamic table
- Model training with feedback at each step
- Personalized course recommendations
- Enhanced UI with emojis, colored headings, and central alignment for a better user experience

## **Installation**

1. **Clone the repository:**
```bash
git clone https://github.com/muhammadadilnaeem/Course-Recommender-App.git
cd Course-Recommender-App
```

2. **Create and activate a virtual environment:**
```bash
python -m venv st_env
st_env\Scripts\activate
```

3. **Install the required packages:**
```bash
pip install -r requirements.txt
```

4. **Ensure you have the datasets in the `data` folder:**
```
Course-Recommender-App/
├── data/
│ ├── ratings.csv
│ ├── sim.csv
│ ├── course_processed.csv
│ └── courses_bows.csv
├── backend.py
├── recommender_app.py
└── requirements.txt
```

## **Running the App**

1. **Run the Streamlit app:**
```bash
streamlit run recommender_app.py
```

2. **Open your web browser and navigate to:**
```
http://localhost:8501
```

## **Code Structure**

- **backend.py:** Contains functions for loading datasets, adding new ratings, and generating course recommendations.
- **recommender_app.py:** The main Streamlit app file. It includes the UI components and integrates the backend functions.

## **Usage**

1. **Select courses you have audited or completed.**
2. **Train the model** using the sidebar options.
3. **Generate course recommendations** by clicking the "Recommend New Courses" button.

## **Customization**

- The app uses emojis and colored headings to enhance the user experience.
- Advanced HTML and CSS are used to style the app.

## Contributing

1. **Fork the repository**
2. **Create a new branch**
3. **Make your changes**
4. **Submit a pull request**

## **License**

This project is licensed under the [MIT License](https://github.com/muhammadadilnaeem/Course-Recommender-App/blob/main/LICENSE).

## **Contact**

For any issues or feature requests, please open an issue on this repository or contact me directly at madilnaeem0@gmail.com.

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