TED Talks Recommendation System with Machine Learning

Overview

This project builds a machine learning-based recommendation system to suggest TED Talks based on user preferences and content similarity.

Features

• Content-Based Filtering: Recommends TED Talks based on text similarity.
• Natural Language Processing (NLP): Utilizes TF-IDF vectorization to analyze talk descriptions.
• Machine Learning Models: Implements various algorithms to improve recommendation accuracy.
• Data Visualization: Provides insights into TED Talks trends.

Dataset

The dataset includes:

• Talk titles, descriptions, and speaker information.
• Tags, views, likes, and other metadata.

Installation & Requirements

Ensure you have Python installed, then install the required dependencies:

pip install pandas numpy scikit-learn matplotlib seaborn nltk

Usage

1. Clone the repository:

   git clone https://github.com/kilofrakh/Ted-Talks-Recommendation-System-with-Machine-Learning.git

2. Navigate to the project directory:

   cd Ted-Talks-Recommendation-System-with-Machine-Learning

3. Run the Jupyter Notebook or Python script to generate recommendations.

Model Performance

• Cosine Similarity for Content-Based Recommendations
• TF-IDF Feature Extraction
• Evaluation using Precision and Recall

Future Enhancements

• Adding collaborative filtering techniques
• Deploying as a web-based recommendation system
• Improving NLP techniques for better recommendations

Contributing

Feel free to fork the repository and submit pull requests with improvements.

License

This project is licensed under the MIT License.

Contact

For queries, contact:

• Abdelkareem Hossam
• Email: [email protected]
• GitHub: kilofrakh