NETFLIX DATASETS ANALYSIS USING PANDAS

Abstract

Netflix is one of the world’s leading subscription-based streaming service providers, offering a vast library of movies, television series, documentaries, and original content across more than 190 countries. As of 2023, Netflix has accumulated over 238 million paid subscribers globally, generating enormous amounts of data about its content catalog, release patterns, genre distributions, and international production trends. The analysis of such data can provide valuable insights into the strategic direction of the platform, audience preferences, and emerging trends in the entertainment industry. This Mini Project presents a comprehensive Exploratory Data Analysis (EDA) of the Netflix dataset sourced from Kaggle. The dataset contains detailed information about approximately 8,807 titles available on Netflix, including attributes such as title name, type (Movie or TV Show), director, cast members, country of production, date added to the platform, release year, rating, duration, genre (listed_in), and a brief description of each title. The primary objective of this project is to analyze the Netflix content library to uncover meaningful patterns, trends, and insights that can inform content strategy, audience targeting, and regional content distribution decisions. The analysis focuses on several key dimensions including the temporal distribution of content additions, the balance between movies and TV shows, geographic content production patterns, genre popularity analysis, content rating distributions, and the identification of most prolific directors and actors on the platform.