This GitHub repository hosts the source code and resources for a robust music recommender system designed to enhance music discovery.

Key Features:

1. Data Collection: Utilize a comprehensive dataset sourced from Kaggle, ensuring rich and diverse content for analysis.
2. Text Preprocessing: Implement advanced text preprocessing techniques to clean and standardize YouTube video descriptions, enhancing the accuracy of subsequent analysis.
3. Feature Extraction: Employ state-of-the-art feature extraction methods, such as TF-IDF and word embeddings, to transform text data into numerical representations suitable for machine learning models.
4. Recommender Model: Implement a powerful recommendation algorithm, with a focus on Content-Based Filtering, to generate accurate and relevant music recommendations based on the content of video descriptions.
5. Content-Based Filtering: Analyze video descriptions to recommend music that aligns with users' preferences, leveraging similarity scores derived from advanced NLP techniques.
6. User Interaction and Recommendations: Enable seamless user interaction by allowing users to input their preferences, such as sample video URLs or keywords related to their interests. Utilize the selected video's description to generate personalized music recommendations, ranked based on similarity scores.