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.
course.recomender.mp4
- 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
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Clone the repository:
git clone https://github.com/muhammadadilnaeem/Course-Recommender-App.git cd Course-Recommender-App
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Create and activate a virtual environment:
python -m venv st_env st_env\Scripts\activate
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Install the required packages:
pip install -r requirements.txt
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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
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Run the Streamlit app:
streamlit run recommender_app.py
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Open your web browser and navigate to:
http://localhost:8501
- 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.
- Select courses you have audited or completed.
- Train the model using the sidebar options.
- Generate course recommendations by clicking the "Recommend New Courses" button.
- The app uses emojis and colored headings to enhance the user experience.
- Advanced HTML and CSS are used to style the app.
- Fork the repository
- Create a new branch
- Make your changes
- Submit a pull request
This project is licensed under the MIT License.
For any issues or feature requests, please open an issue on this repository or contact me directly at madilnaeem0@gmail.com.