Netflix Data Analysis Project 🎬

Overview

This project performs Exploratory Data Analysis (EDA) on Netflix datasets using Python.
The analysis focuses on content distribution, genres, ratings, countries, movie durations, and Netflix growth trends over the years.

The project was built using Jupyter Notebook and various Python data analysis libraries.

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Technologies Used

• Python
• Pandas
• Matplotlib
• Seaborn
• Jupyter Notebook
• Git & GitHub

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Project Structure

Netflix-Data-Analysis/
│
├── data/
│   └── netflix_titles.csv
│
├── output/
│   ├── movies_vs_tvshows.png
│   ├── content_over_years.png
│   ├── top_genres.png
│   ├── top_countries.png
│   ├── content_ratings.png
│   ├── movie_duration.png
│   ├── top_directors.png
│   └── top_actors.png
│
├── netflix_analysis.ipynb
├── README.md
├── requirements.txt
└── .gitignore

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Analysis Performed

📌 Movies vs TV Shows Analysis

Compared the number of Movies and TV Shows available on Netflix.

📌 Content Added Over the Years

Analyzed Netflix content growth and expansion trends over time.

📌 Top Genres on Netflix

Identified the most popular genres available on the platform.

📌 Top Countries Producing Netflix Content

Explored which countries contribute the highest amount of content.

📌 Netflix Content Ratings Analysis

Studied the distribution of ratings such as TV-MA, TV-14, PG-13, and more.

📌 Movie Duration Analysis

Analyzed the distribution of movie durations on Netflix.

📌 Most Frequent Directors

Identified directors with the highest number of titles on Netflix.

📌 Most Featured Actors

Explored actors who appear most frequently in Netflix content.

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Sample Visualizations

Movies vs TV Shows

Netflix Content Added Over the Years

Top Genres on Netflix

Top Countries Producing Netflix Content

Netflix Content Ratings

Movie Duration Analysis

Most Frequent Directors

Most Featured Actors

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Key Insights

• Movies dominate Netflix content compared to TV Shows.
• Netflix experienced rapid content growth after 2015.
• International Movies and Dramas are among the most popular genres.
• The United States contributes the highest amount of Netflix content.
• TV-MA and TV-14 are the most common content ratings.
• Indian actors and directors appear frequently on the platform.

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Learning Outcomes

Through this project, I improved my understanding of:

• Data Cleaning
• Exploratory Data Analysis (EDA)
• Data Visualization
• GroupBy & Aggregation
• Working with real-world datasets
• Git & GitHub workflow

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Author

Sujay Pandit

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