Netflix Content Strategy Analysis 🎬

Welcome to the second project of the "21 Projects, 21 Days" GFG course. Today's project moves beyond basic EDA into in-depth analysis of streaming data, focusing on time-series trends, content distribution, and global production.

📌 Project Objective

The goal is to analyze the Netflix dataset to uncover how the platform's content strategy has evolved. We explore:

• Trends in content production and release over time.
• The balance between Movies and TV Shows.
• Regional contributions to the Netflix library.
• Analysis of genres and content maturity ratings.

🛠️ Concepts & Tools Used

• Data Cleaning: Handling significant missing values in columns like director, cast, and country.
• Time-Series Analysis: Converting string dates into datetime objects to analyze monthly and yearly upload trends.
• Text Processing: Parsing multi-value strings in the listed_in (genres) column.
• Advanced Visualization: Using Seaborn and Matplotlib for distribution plots and WordCloud for genre visualization.

📊 Key Insights from Analysis

• Library Composition: Approximately 70% of Netflix content consists of Movies, while 30% are TV Shows.
• Growth Trend: There was a massive surge in content additions starting around 2015, peaking between 2018 and 2019.
• Content Age: Most content on Netflix is relatively new, with a significant portion released after 2010.
• Ratings Distribution: The most common rating is TV-MA, indicating a heavy focus on mature audiences.

🚀 Repository Structure

Project-Netflix_Content_Dataset_EDA/
├── Project-Netflix_Content_Dataset_EDA.ipynb   # Main analysis notebook
├── netflix_titles.csv                          # Dataset (if applicable)
└── README.md                                   # Project documentation