Amazon Prime Video - Exploratory Data Analysis

📊 Project Overview

Comprehensive EDA on Amazon Prime Video's content catalog analyzing 9,871 titles to uncover strategic insights about content diversity, regional production, temporal trends, and quality metrics.

🎯 Objectives

• Analyze content type distribution (Movies vs TV Shows)
• Identify dominant genres and gaps
• Examine regional production patterns
• Track content growth over time
• Evaluate quality through IMDb/TMDB ratings

📁 Dataset

• titles.csv: 9,871 titles with 15 features
• credits.csv: 124,235 cast/crew records

🛠️ Tech Stack

Python | Pandas | NumPy | Matplotlib | Seaborn | Jupyter Notebook

📈 Key Findings

• Content Split: 80% movies, 20% TV shows
• Top Genres: Drama (3,000+), Comedy (2,500+)
• Peak Growth: 2018 with 700+ titles added
• Regional: 70% US-produced content
• Quality: Average IMDb rating 7.2/10

💼 Business Recommendations

1. Rebalance toward more TV shows for engagement
2. Invest in underrepresented genres (Thriller, Sci-Fi)
3. Diversify regional production beyond US
4. Focus on premium quality content (8.0+ ratings)
5. Implement data-driven content curation

🚀 How to Run

pip install pandas numpy matplotlib seaborn
jupyter notebook Amazon_Prime_EDA_RaviShankarKumar.ipynb

👨‍💻 Author

Ravi Shankar Kumar
BTech CSE (AI/ML)

Email: rshankarkumar906@gmail.com

📄 License

MIT License