🎵 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 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.

Exploratory analysis of Amazon Prime Video’s global catalog, highlighting trends in content distribution, genres, audience ratings, and release patterns using a Kaggle dataset.

An Exploratory Data Analysis (EDA) of Netflix's 2021 content catalog using the Kaggle dataset. This project covers data cleaning, content categorization, and temporal and geographic insights. The analysis explores trends in Netflix's movies and TV shows, including ratings, genres, release patterns, and geographic production distribution.

📈 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.

Data analysis and visualization of Netflix’s catalog (2011–2025) using Kaggle datasets. The project explores content growth, genres, ratings, popularity, durations, and global distribution, with interactive dashboards highlighting key trends.

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