https://github.com/rezowanrahat/netflix_analysis
Data analysis of Netflix content using Python, Pandas, and Seaborn
https://github.com/rezowanrahat/netflix_analysis
data-analysis data-visualization netflix pandas python
Last synced: about 2 months ago
JSON representation
Data analysis of Netflix content using Python, Pandas, and Seaborn
- Host: GitHub
- URL: https://github.com/rezowanrahat/netflix_analysis
- Owner: rezowanrahat
- License: mit
- Created: 2025-06-19T06:23:20.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-19T06:44:29.000Z (about 1 year ago)
- Last Synced: 2025-06-19T07:32:03.586Z (about 1 year ago)
- Topics: data-analysis, data-visualization, netflix, pandas, python
- Language: Jupyter Notebook
- Homepage:
- Size: 1.67 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🎬 Netflix Movies and TV Shows Analysis
A data analytics project exploring trends, genres, and global reach of Netflix content using Python, Pandas, and Seaborn.
---
## 📊 Project Overview
This project analyzes a publicly available Netflix dataset from Kaggle, with the goal of identifying content trends such as:
- Distribution of Movies vs TV Shows
- Trends in content releases by year
- Most popular content ratings (PG, R, etc.)
- Top countries producing Netflix content
- Most common genres
---
## 🧰 Technologies Used
- Python
- Pandas & NumPy
- Matplotlib & Seaborn
- Jupyter Notebook / Google Colab
---
## 📁 Dataset
**Source**: [Netflix Movies and TV Shows | Kaggle](https://www.kaggle.com/datasets/shivamb/netflix-shows)
**File**: `netflix_titles.csv`
---
## 📌 Key Findings
- 🧮 Netflix has more Movies than TV Shows (~70%).
- 🌍 The United States is the leading producer of Netflix content.
- 🕰️ Content release peaked around 2018-2019.
- 📺 Most shows are rated TV-MA or TV-14.
- 🎭 Dramas are the most common genre.
> Visuals are included in the notebook for deeper insights.
---
## 📸 Sample Visualizations
*Add your plots as images using the Jupyter “Save As PNG” function and upload to a `images/` folder.*
---
## 🚀 How to Run
1. Clone this repository
2. Install the requirements (optional)
3. Open the notebook `netflix_analysis.ipynb`
4. Run all cells to view the analysis
---
## 🧠 Future Work
- Perform sentiment analysis on Netflix descriptions
- Integrate IMDb ratings for deeper insights
- Create an interactive dashboard (e.g., with Plotly or Dash)
---
## 📄 License
This project is under the MIT License. Dataset is publicly available via Kaggle.
---
## 🙋♂️ Author
**Rezowan Khan**
- GitHub: [@rezowanrahat](https://github.com/rezowanrahat )
- LinkedIn: [Rezowan Khan](https://www.linkedin.com/in/rezowan-khan/)
---
## 🏷️ Tags
`Python` • `Pandas` • `Netflix` • `Beginner Project` • `Data Analysis` • `Seaborn` • `EDA`