🎬 Netflix Movies & TV Shows — Exploratory Data Analysis

Exploratory Data Analysis on Netflix's content library — uncovering trends in content type, genres, ratings, country contributions, and release patterns to understand how Netflix has built its global entertainment empire.

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📌 Table of Contents

• Project Overview
• Dataset Description
• Tools & Technologies
• Project Structure
• Key Analysis Performed
• Key Insights
• Visualizations
• Conclusions
• Future Work
• Author

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🔍 Project Overview

Netflix is the world's leading streaming platform with 200M+ subscribers across 190+ countries. Understanding what kind of content Netflix produces, from where, for whom, and when is a powerful business intelligence exercise.

This project performs a comprehensive Exploratory Data Analysis (EDA) on Netflix's titles dataset to:

• Understand the split between Movies and TV Shows
• Identify dominant genres, ratings, and content categories
• Analyze year-over-year content growth trends
• Explore country-level content contributions
• Discover top directors and most-featured cast members
• Extract actionable insights about Netflix's content strategy

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📁 Dataset Description

📂 Source: Kaggle — Netflix Movies and TV Shows

The dataset contains 8,807 titles available on Netflix as of mid-2021.

Feature	Description
show_id	Unique ID for each title
type	Movie or TV Show
title	Name of the title
director	Director(s)
cast	Featured cast members
country	Country of production
date_added	Date added to Netflix
release_year	Original release year
rating	Content rating (TV-MA, PG-13, etc.)
duration	Duration in minutes (movies) or seasons (TV shows)
listed_in	Genre(s)
description	Brief description

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🛠️ Tools & Technologies

• Language: Python 3.8+
• Libraries: Pandas, NumPy, Matplotlib, Seaborn
• Environment: Jupyter Notebook

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📂 Project Structure

Netflix_EDA/
│
├── Netflix_EDA.ipynb                    # Main analysis notebook
├── Netflix_EDA_Report.pdf               # Detailed PDF report
├── README.md                            # Project documentation
├── netflix_titles.csv                   # Dataset (download from Kaggle)
└── visualizations/
    ├── viz_content_type.png
    ├── viz_rating_dist.png
    ├── viz_top_genres.png
    ├── viz_rating_by_type.png
    ├── viz_movie_duration.png
    ├── viz_yearly_additions.png
    ├── viz_monthly_pattern.png
    ├── viz_top_countries.png
    └── viz_directors_cast.png

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📈 Key Analysis Performed

• Data Cleaning & Preprocessing — Handling missing values, date parsing, feature extraction
• Content Type Analysis — Movies vs TV Shows distribution
• Rating Analysis — Distribution of content ratings across types
• Genre Analysis — Top genres and multi-genre breakdown
• Bivariate Analysis — Rating by content type, duration patterns
• Trend Analysis — Year-over-year and month-wise content additions
• Geographic Analysis — Top content-producing countries
• People Analysis — Top directors and most-featured cast members

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🔑 Key Insights

1. 🎬 Content Split — Netflix's library is ~69% Movies and ~31% TV Shows, showing a clear preference for film content
2. ⭐ Ratings — TV-MA and TV-14 dominate — Netflix is primarily an adult content platform
3. 🎭 Genres — International Movies, Dramas, and Comedies are the top three genres, reflecting Netflix's global expansion strategy
4. 📅 Growth Peak — Content additions peaked in 2019-2020; a dip in 2021 likely reflects COVID-19 production slowdowns
5. 📆 Monthly Pattern — January and July see the highest content additions — aligning with post-holiday and summer viewing peaks
6. 🌍 Geographic Dominance — The USA contributes the most content by a large margin; India ranks 2nd, highlighting strong investment in South Asian markets
7. 🎞️ Movie Duration — Average Netflix movie is ~99 minutes; most fall in the 80–120 minute sweet spot
8. 🎥 Directors — A small group of prolific directors contributes a disproportionately large share of Netflix's catalog

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📊 Visualizations

The notebook includes 9 visualizations:-

• Content type pie chart & bar chart
• Rating distribution bar chart
• Top 15 genres horizontal bar chart
• Rating by content type grouped bar chart
• Movie duration histogram with mean/median lines
• Yearly content addition trend (2010–2021)
• Monthly content addition line chart
• Top 12 countries bar chart
• Top 10 directors & cast side-by-side comparison

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✅ Conclusions

This analysis reveals that Netflix's content strategy is built on:

• Volume over niche — massive catalog across all genres and ratings
• Global-first thinking — heavy investment in international content, especially from India
• Adult audience focus — majority of content is TV-MA or TV-14
• Aggressive growth — near-exponential content additions from 2015 to 2020
• Seasonal strategy — content drops timed around January and July for maximum viewership

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🚀 Future Work

• Sentiment analysis on title descriptions using NLP
• Recommendation system based on genres and ratings
• Time-series forecasting of content addition trends
• Comparison with competitor platforms (Disney+, Prime Video)

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👤 Author

Mayank Yadav

• 🔗 LinkedIn
• 🐙 GitHub
• 📧 mayankyadavj589@gmail.com

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