This project focuses on analyzing a dataset of Netflix shows and movies using data analytics and visualization techniques. The goal is to extract meaningful insights such as genre distribution, release trends, top countries producing content, and content ratings.
- Python (Pandas, NumPy, Matplotlib, Seaborn)
- Jupyter Notebook
- Data Cleaning & Visualization
- Git & GitHub for version control
- Source: https://drive.google.com/file/d/1cWcK8cddROe_DSv5zH5Fk7od32tK3ftf/view
- Attributes: Title, Director, Cast, Country, Date Added, Release Year, Rating, Duration, Genre, Description
- Total number of shows and movies on Netflix
- Year-wise content release trends
- Country-wise content production
- Genre distribution
- Rating-wise content classification
- Most frequent directors and actors
- Bar charts for yearly content addition
- Pie chart for content type ratio (Movie vs TV Show)
- Heatmap of missing values
- Histogram for duration distribution
- Cleaned and preprocessed missing values
- Used groupby and datetime operations for temporal analysis
- Created visually appealing and informative charts
- Delivered actionable insights for content strategists
Netflix-Data-Analysis/
- netflix_analysis.ipynb # Main analysis notebook
- netflix_titles.csv # Raw dataset
- README.md # Project documentation
- visualizations/ # Folder for saved plots
- requirements.txt # Python dependencies
This project is for learning purposes and is not intended for commercial use.
Feel free to fork and experiment.
- Name: YENUGANTI HARI KRISHNA
- Role: Data Analyst & Student
- Platform: [Unified Mentor]
- LinkedIn: https://www.linkedin.com/in/yenuganti-hari-krishna-95496829b?