This project is a capstone built while learning Matplotlib, inspired by this tutorial video. It explores Netflix’s catalog using data visualization techniques with Python.
Netflix Movies and TV Shows Dataset from Kaggle:
📎 https://www.kaggle.com/datasets/shivamb/netflix-shows
- Understand the distribution of content types (Movies vs TV Shows)
- Analyze release trends by year and month
- Explore countries contributing most content
- Visualize most popular genres and durations
- Highlight missing metadata (directors, cast, etc.)
- Python
- Jupyter Notebook
- Pandas – data wrangling
- Matplotlib (pyplot) – static plotting
- Movies dominate over TV Shows in total titles available
- Most content is produced in the United States, with India and the UK also contributing significantly
- Content additions peaked in 2019–2020, indicating rapid platform growth
- Genres like Documentaries, Dramas, and Comedies are most frequent
- Many entries lack complete metadata, which could impact recommendation systems
📦 Netflix-Matplotlib-Capstone ┣ 📄 netflix_titles.csv # Dataset ┣ 📄 Netflix_Analysis.ipynb # Jupyter Notebook with all code & plots ┣ 📄 README.md # This file
- Strengthened my Matplotlib fundamentals
- Learned how to extract insights from real-world data
- Gained experience in visual storytelling for analytics
- Add Seaborn/Plotly for improved visual aesthetics
- Make an interactive dashboard (Streamlit or Dash)
- Add genre clustering or NLP on descriptions
If you have suggestions, feedback, or want to collaborate, feel free to connect via LinkedIn.
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