https://github.com/sabaasif2501/netflix-data-analysis
Exploratory data analysis of Netflix content using Python and pandas. Content types, genres, countries, and release years.
https://github.com/sabaasif2501/netflix-data-analysis
data-analysis netflix pandas portfolio-project python
Last synced: about 2 months ago
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Exploratory data analysis of Netflix content using Python and pandas. Content types, genres, countries, and release years.
- Host: GitHub
- URL: https://github.com/sabaasif2501/netflix-data-analysis
- Owner: sabaasif2501
- Created: 2025-06-14T10:03:09.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-14T10:29:39.000Z (about 1 year ago)
- Last Synced: 2025-06-21T16:05:20.975Z (about 1 year ago)
- Topics: data-analysis, netflix, pandas, portfolio-project, python
- Language: Jupyter Notebook
- Homepage:
- Size: 1.5 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 📊 Netflix Data Analysis with Python & Pandas
"Exploratory data analysis of Netflix content using Python and pandas. Focused on content types, top countries, release years, and popular genres."
## 🔧 Tools Used
- Python
- pandas
- Google Colab / Jupyter Notebook
## 📁 Dataset
- [Netflix Titles Dataset](https://www.kaggle.com/datasets/shivamb/netflix-shows)
---
## 📌 Key Questions Answered
### 1. 🎬 What type of content dominates Netflix?
> ✅ **Movies** are more common (6,131) than TV Shows (2,676)
### 2. 🌍 Which countries produce the most content?
> 🇺🇸 United States: 2,818 titles
> 🇮🇳 India: 972 titles
> 🇬🇧 UK: 419 titles
> 🇯🇵 Japan: 245 titles
### 3. 📅 In which years were most titles released?
> Top years: 2018 (1,147), 2017 (1,032), 2019 (1,030), 2020 (953), 2016 (902)
### 4. 🎭 What are the most popular genres?
> - Dramas, International Movies
> - Documentaries
> - Stand-Up Comedy
---
## 📈 Future Improvements
- Add visualizations using Matplotlib/Seaborn
- Clean unknown country values
- Create an interactive dashboard with Plotly
---
## 🧠 My Learning
This was my first real-world data project. I used pandas to filter, clean, and analyze structured data. It helped me build confidence in Python, data exploration, and communicating results.