A comprehensive Exploratory Data Analysis (EDA) project on the Netflix Movies and TV Shows dataset using Python. This project uncovers trends in Netflix's content library through data cleaning, preprocessing, and insightful visualizations.
🏅 This notebook received a Kaggle Bronze Medal for its quality of analysis and presentation.
This project analyzes Netflix's catalog to answer questions such as:
- Which countries produce the most Netflix content?
- How has Netflix's content library grown over time?
- What is the ratio of Movies to TV Shows?
- What are the most common ratings?
- Which genres dominate the platform?
- How has Netflix expanded globally?
The project follows a complete EDA workflow including data cleaning, feature engineering, visualization, and insight generation.
- Dataset: Netflix Movies and TV Shows
- Source: Kaggle
- Records: ~8,800 titles
- Features: 12
Some important columns include:
- Title
- Type
- Director
- Cast
- Country
- Date Added
- Release Year
- Rating
- Duration
- Listed In (Genre)
- Description
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Jupyter Notebook
The notebook includes:
- Data Cleaning
- Missing Value Analysis
- Duplicate Detection
- Data Type Conversion
- Feature Engineering
- Country-wise Analysis
- Genre Analysis
- Rating Distribution
- Release Year Trends
- Movies vs TV Shows Comparison
- Data Visualization
Approximately 70% of Netflix's content consists of Movies, while 30% are TV Shows.
The United States contributes significantly more titles than any other country, followed by India and the United Kingdom.
Netflix experienced rapid content expansion between 2015–2019, reaching its highest number of releases before declining slightly in later years.
Netflix-data-Analysis/
│
├── images/
│ ├── movies_vs_tv.png
│ ├── top_countries.png
│ └── content_over_years.png
│
├── netflix-data-analysis.ipynb
├── netflix_titles.csv
└── README.md
- Clone the repository
git clone https://github.com/RugvedBane/Netflix-data-Analysis.git- Navigate into the project
cd Netflix-data-Analysis- Install the required libraries
pip install pandas numpy matplotlib seaborn notebook- Launch Jupyter Notebook
jupyter notebook- Open
netflix-data-analysis.ipynb
- Exploratory Data Analysis (EDA)
- Data Cleaning
- Data Visualization
- Feature Engineering
- Statistical Analysis
- Business Insight Generation
- Python for Data Analysis
🥉 Awarded a Kaggle Bronze Medal for this notebook.
The project was recognized for its analytical approach, visualizations, and presentation on Kaggle.
Rugved Bane
⭐ If you found this project useful, consider giving it a Star.