🔗 https://netflix-eda-feature-engineering-6vzr5ypnrbsb2bbew5ctdv.streamlit.app/
An end-to-end data analytics and machine learning project analyzing Netflix content using EDA, feature engineering, interactive visualizations, and predictive modeling.
Built with Streamlit to provide a clean, interactive dashboard experience.
- 📊 Interactive Streamlit dashboard
- 🎞️ Movies vs TV Shows analysis
- 🌍 Country-wise & Genre-wise insights
- 📈 Year-wise content growth trends
- 🤖 Linear Regression model for duration prediction
- 🧠 Feature engineering on real-world data
- 🧪 Multi-page Streamlit application
- Python
- Pandas, NumPy
- Matplotlib, Seaborn
- Streamlit
- Scikit-learn
- VS Code
Netflix-EDA-Feature-Engineering/
│
├── app.py # Main Streamlit app
├── pages/ # Multi-page Streamlit views
├── notebooks/ # EDA & feature engineering notebooks
├── data/ # Netflix dataset
├── visuals/ # Saved plots & screenshots
├── insights.md # Business insights
├── requirements.txt
└── README.md
- Model Used: Linear Regression
- Target: Content Duration
- Metrics:
- R² Score
- Mean Absolute Error (MAE)
The model demonstrates explainability and baseline predictive performance.
pip install -r requirements.txt
streamlit run app.py
📌 Dataset
Netflix Movies and TV Shows dataset (public dataset).
