<title>Netflix Exploratory Data Analysis (EDA)</title>

Netflix Exploratory Data Analysis (EDA)

Dataset

This repository contains an Exploratory Data Analysis (EDA) of the Netflix dataset. The goal of this analysis was to gain insights into the content available on Netflix, understand viewing trends, and explore patterns in genres, countries, and durations.

Analysis Overview

In this EDA, the following aspects were explored:

• Distribution of movies and TV shows on Netflix
• Top 5 countries with the most Netflix viewers
• Content catering to different age groups
• Trends in the growth of Netflix content over the years
• Comparison of movie and TV show counts
• Monthly variation in content additions
• Popular genres in movies and TV shows
• Duration analysis for movies and TV shows

Key Findings

Here are some key findings from the analysis:

• There are more movies than TV shows on Netflix.
• The top 5 countries with the most Netflix viewers are the United States, India, the United Kingdom, Japan, and South Korea.
• Content on Netflix caters mostly to adults and then teens, for both movies and TV shows.
• The growth of Netflix content has been significant, with a steady increase until 2018. However, it declined since 2019, possibly due to the impact of COVID-19 and the launch of Disney Plus.
• Popular genres in movies and TV shows include international movies, dramas, and comedies.
• Most movies on Netflix have a duration between 80-120 minutes, with very few exceeding 150 minutes.
• TV shows on Netflix predominantly have only Season 1 available.

Repository Structure

• notebooks/: Jupyter Notebook containing the code and detailed analysis steps.
• netflix_titles.csvgit/: Folder containing the Netflix dataset used for the analysis.
• images/: Folder containing visualizations and charts generated during the analysis.
• README.md: This file, providing an overview of the analysis.

How to Use

To explore the analysis and findings, follow these steps:

1. Clone this repository to your local machine.
2. Install the necessary dependencies (Python, Jupyter Notebook, pandas, matplotlib, plotly, etc.).
3. Open the Jupyter Notebook to view the analysis.
4. Run the notebook cells to reproduce the visualizations and explore the findings.

Feel free to modify and adapt the code or use the analysis as a reference for your own projects.