https://github.com/sayed-ashfaq/netflix_datacleaning
Netflix data analysis highlights significant null values, requiring cleaning and visualization to uncover viewer trends and regional insights. Improving data quality, personalizing recommendations, and targeting untapped markets can drive growth and profitability.
https://github.com/sayed-ashfaq/netflix_datacleaning
matplotlib-pyplot numpy-library pandas-library python seaborn-plots
Last synced: 10 months ago
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Netflix data analysis highlights significant null values, requiring cleaning and visualization to uncover viewer trends and regional insights. Improving data quality, personalizing recommendations, and targeting untapped markets can drive growth and profitability.
- Host: GitHub
- URL: https://github.com/sayed-ashfaq/netflix_datacleaning
- Owner: sayed-ashfaq
- Created: 2024-12-22T02:21:06.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-22T02:29:06.000Z (over 1 year ago)
- Last Synced: 2025-01-03T23:34:39.479Z (over 1 year ago)
- Topics: matplotlib-pyplot, numpy-library, pandas-library, python, seaborn-plots
- Homepage:
- Size: 2.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Netflix Dataset Analysis
## About Netflix
Netflix is one of the world's most popular media and video streaming platforms. As of mid-2021, Netflix offers over **10,000 movies and TV shows** and has amassed over **222 million subscribers globally**.
This dataset contains a detailed listing of all movies and TV shows available on Netflix, including attributes like cast, directors, ratings, release year, duration, and more. By analyzing this dataset, we aim to generate insights that can help Netflix make data-driven decisions for future content production and business expansion.
---
## Business Problem
The primary goal is to analyze the dataset and derive actionable insights that could help Netflix:
1. Decide which type of shows/movies to produce.
2. Strategize growth opportunities in different countries.
---
### **Dataset Details**
The dataset consists of a comprehensive list of TV shows and movies available on Netflix. Below are the attributes included:
- **`Show_id`**: Unique ID for every Movie/TV Show.
- **`Type`**: Identifier specifying whether it's a Movie or TV Show.
- **`Title`**: Title of the Movie/TV Show.
- **`Director`**: Director of the Movie.
- **`Cast`**: Actors involved in the Movie/Show.
- **`Country`**: Country where the Movie/Show was produced.
- **`Date_added`**: Date the content was added to Netflix.
- **`Release_year`**: The year the content was originally released.
- **`Rating`**: TV Rating of the content (e.g., PG, R, TV-MA).
- **`Duration`**: Total duration, either in minutes (for movies) or the number of seasons (for TV shows).
- **`Listed_in`**: Genre(s) the content belongs to.
- **`Description`**: A brief summary of the content.
---
## Objective
The analysis will focus on:
- Understanding the distribution of content across genres, ratings, and countries.
- Identifying trends in content addition and production.
- Generating actionable insights to guide Netflix in producing shows/movies and expanding its presence in different regions.
---
## Tools and Libraries
For analysis, you can use:
- **Python Libraries**:
- `Pandas` for data manipulation.
- `Matplotlib` and `Seaborn` for visualization.
- `NumPy` for numerical operations.
- `Scikit-learn` for machine learning models, if applicable.
---
## Future Scope
This project can be extended by:
- Building recommendation systems based on user preferences.
- Predicting trends in viewership for future Netflix productions.
- Exploring the correlation between ratings, genres, and regional success.
---
## License
This project is open for academic and non-commercial purposes. Ensure to credit the dataset source where applicable.