Netflix Data Analysis

A data analysis project based on the Netflix dataset using Python libraries like Pandas, NumPy, Matplotlib, and Seaborn. This project explores Netflix content trends, including movies vs TV shows, content distribution by country, ratings, genres, release years, and more through data cleaning, visualization, and exploratory data analysis (EDA).

Data ScienceJupyter NotebookMIT

Abstract

Netflix Data Analysis is an open-source Data Science project. A data analysis project based on the Netflix dataset using Python libraries like Pandas, NumPy, Matplotlib, and Seaborn. This project explores Netflix content trends, including movies vs TV shows, content distribution by country, ratings, genres, release years, and more through data cleaning, visualization, and exploratory data analysis (EDA). A complete Exploratory Data Analysis (EDA) project on the Netflix dataset using Python. This project focuses on analyzing Netflix movies and TV shows to discover trends, popular genres, ratings distribution, country-wise content production, and yearly growth patterns through data visualization. It is built using Jupyter Notebook. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building a Data Science mini project or final-year project.

1. Introduction

A complete Exploratory Data Analysis (EDA) project on the Netflix dataset using Python. This project focuses on analyzing Netflix movies and TV shows to discover trends, popular genres, ratings distribution, country-wise content production, and yearly growth patterns through data visualization.

2. Objective

A data analysis project based on the Netflix dataset using Python libraries like Pandas, NumPy, Matplotlib, and Seaborn. This project explores Netflix content trends, including movies vs TV shows, content distribution by country, ratings, genres, release years, and more through data cleaning, visualization, and exploratory data analysis (EDA).

This project demonstrates how Jupyter Notebook can be applied to a real-world Data Science problem.

4. Technology Stack

Jupyter Notebook
  • Matplotlib

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
  • pip for dependencies
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/faizanfatmi/Netflix-Data-Analysis.git
cd Netflix-Data-Analysis

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Turn the analysis into an interactive dashboard
  • Automate data refresh with a scheduled job
  • Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What is the source of the dataset and how was missing data handled?
  2. Which exploratory analysis steps revealed the most useful insight?
  3. Why were these particular charts chosen to present the data?
  4. Which statistical or ML technique supports the conclusions?
  5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by faizanfatmi and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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