An Exploratory Data Analysis (EDA) project on Netflix's content catalog using Python and Plotly. Analyzes content trends, genre popularity, country contributions, director insights, and rating patterns across 7,789 titles from 2008 to 2021.
- Analyze Netflix content distribution across Movies and TV Shows
- Identify top genres, directors, and content-producing countries
- Understand rating patterns and target audience segmentation
- Track content growth trends from 2008 to 2021
| Property | Value |
|---|---|
| Records | 7,789 |
| Columns | 11 |
| Coverage | 2008 – 2021 |
| Fields | Title, Type, Director, Cast, Country, Rating, Duration, Genre, Year |
- Python
- Pandas
- Plotly Express (interactive charts)
- Matplotlib / Seaborn
- Google Colab
netflix-data-analysis/
├── Netflix Dataset.csv # Dataset file
├── Netflix_Dataset_Analysis.ipynb # Main analysis notebook
├── Netflix_Project_Problem_Statement.docx # Project problem statement
├── requirements.txt # Python dependencies
└── README.md
| Chart | Description |
|---|---|
| Movies vs TV Shows | Distribution and yearly breakdown of content type |
| Top Genres | Most frequent genres across the catalog |
| Top 10 Directors | Raúl Campos & Jan Suter lead with 18 titles |
| Country-wise Contributions | Top content-producing countries |
| Ratings Distribution | Breakdown of content ratings (TV-MA, TV-14, etc.) |
| Movie Duration | Histogram of movie lengths in minutes |
| Content Growth | Movies vs TV Shows released per year (2008–2021) |
- Netflix catalog is movie-heavy with significant TV Show growth post-2015
- Raúl Campos & Jan Suter are the most prolific directors with 18 titles
- United States is the top content-contributing country
- Content additions peaked around 2018–2019
- Most content is rated TV-MA and TV-14, targeting adult audiences
git clone https://github.com/Subi121/netflix-data-analysis.git
cd netflix-data-analysis
pip install -r requirements.txt
jupyter notebook Netflix_Dataset_Analysis.ipynbOr open directly in Google Colab:
- Open the notebook Netflix_Dataset_Analysis.ipynb in Google Colab
- Upload
Netflix DataSet.csv - Run all cells to generate interactive charts
- Sentiment analysis on titles and descriptions
- Predict content type using ML classification
- Time-series forecasting of content growth
- Add genre-based recommendation system
- This is an independent data analysis project completed during an internship at VOIS (Vodafone Intelligent Solutions).
- Not affiliated with or endorsed by Netflix, Inc.
- Dataset was provided as part of the internship program.
This project is licensed under the MIT License.