Project Section Template

1. Introduction:

   • Welcome to our Course Recommendation System Project!
   • Our system assists users in finding relevant courses based on their interests and preferences.

2. Objective:

   • Develop a recommendation system to enhance users’ learning experience by suggesting courses tailored to their needs.

3. Dataset Description:

   • Utilized a dataset containing course information such as titles, descriptions, categories, and user ratings.
   • Sourced the dataset from [source], a platform specializing in online courses.

4. Data Preprocessing:

   • Cleaned the dataset, handled missing values, and transformed categorical variables for analysis.

5. Recommendation Techniques:

   • Implemented collaborative filtering and content-based filtering techniques to generate course recommendations.
   • Collaborative filtering analyzes user behavior to suggest similar courses, while content-based filtering recommends based on course attributes.

6. Implementation Details:

   • Developed using Python and libraries such as Pandas, NumPy, and Scikit-learn.
   • Built the system in a Jupyter Notebook, providing step-by-step implementation and analysis.

7. User Interface:

   • Features a user-friendly interface allowing users to input preferences and receive personalized recommendations.
   • Users can specify interests, categories, and desired course attributes for tailored suggestions.

8. Evaluation Metrics:

   • Used accuracy, precision, and recall to evaluate system performance.
   • Results demonstrate the effectiveness of the system in providing relevant course suggestions.

9. Future Enhancements:

   • Potential enhancements include advanced machine learning techniques, user feedback integration, and dataset expansion.

10. Conclusion:

    • The Course Recommendation System effectively assists users in discovering relevant courses.
    • We hope the system enhances your learning journey and encourages exploration of new courses.

11. References:

    • Citations or links to relevant resources, datasets, and libraries.

12. Acknowledgments:

    • Thanks to individuals/organizations for their support and contributions.

Demo:

<iframe src="https://abc.streamlit.app/?embed=true&embed_options=dark_theme" width="100%" height="600" frameborder="0" scrolling="no"></iframe>

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