This project explores a Netflix dataset focused on subscription data, user distribution, and country-level statistics. The goal is to understand how a global platform like Netflix expands, adapts, and performs across different regions.
Using Python and libraries like pandas, matplotlib, seaborn, and plotly, I analyzed:
- 📈 Subscriber Trends by Country: Visualizing which countries contribute the most to Netflix's global subscriber base.
- 💰 Revenue Patterns Across Regions: Observing how Netflix's earnings vary geographically.
- 📊 ARPU (Average Revenue Per User): Comparing user value across different countries and regions.
- 🧾 Plan Types and Pricing Distribution: Analyzing the variety and cost of subscription models globally.
- 🚀 Growth Rate Trends: Tracking how fast Netflix has scaled over the years in specific areas.
Throughout this project, I sharpened several key data science skills:
- Data Cleaning & Preprocessing: Dealt with missing values, inconsistent formats, and outliers.
- Time Series & Group-Based Analysis: Aggregated metrics by year, country, and plan type.
- Effective Data Visualization: Created insightful and clear plots to communicate findings.
- Contextual Thinking: Interpreted data with an understanding of economic and cultural factors impacting tech adoption.
This project reinforced a powerful lesson: simple numbers can tell deep, complex stories. Netflix’s performance varies dramatically across regions due to culture, pricing strategy, infrastructure, and user behavior.
Understanding these patterns isn't just about plotting charts—it's about interpreting them within real-world context.
If you’re working on beginner-friendly data projects or curious about the business side of tech platforms, I’d love to connect and learn together!
The dataset used in this analysis contains country-level Netflix statistics including subscriber counts, revenue, ARPU, plan types, and historical data by year.
Note: Data source and license info (if applicable) can be added here.
- Python
- Pandas
- Matplotlib
- Seaborn
- Plotly
- Jupyter Notebook
- Clone this repository:
git clone https://github.com/yourusername/netflix-eda.git cd netflix-eda
Open the Jupyter Notebook and run the analysis step by step.
✅ Future Work Incorporate more recent data
Add regional comparisons with competitors (e.g., Disney+, Amazon Prime)
Create an interactive dashboard (e.g., with Streamlit or Tableau)
Let me know if you'd like a matching Jupyter Notebook header, badge suggestions, or want to add deployment options like Streamlit or a dashboard!