This project analyzes Netflix's content library using SQL. It explores content type distribution, rating trends, country-wise content availability, and genre classification to extract meaningful insights from Netflix data for better analysis.

🎵 A Python-based content recommendation system utilizing ML algorithms and matrix factorization techniques to analyze 600k-song dataset. Combines SVD, NMF, Factorization Machines, and Direct Similarity for personalized music suggestions. Handles cold start, optimizes with weighted similarity, and includes tools for visualization & evaluation.

📈 This project explores Netflix's movie and TV show dataset using SQL to uncover insights about content trends, ratings, genres, and release patterns. The analysis includes data cleaning, querying, and visualization to understand Netflix's content strategy.

Interactive Power BI dashboard analyzing Netflix titles, viewing patterns, and strategic recommendations.

Annotation guidelines for using the X-GENRE schema for the development of genre-annotated text datasets