sahascodes / exploratory-data-analysis-on-netflix-content

Netflix Exploratory Data Analysis (EDA)

Overview

This repository contains code for an Exploratory Data Analysis (EDA) project on Netflix data. The analysis explores various aspects of the dataset, including importing data, basic statistics, data cleaning, and visualization of Netflix content trends. The project aims to provide insights into the distribution of genres, top actors, and trends in the release of TV shows and movies on Netflix.

Project Structre

• Netflix_eda.ipynb: Jupyter Notebook containing the code for EDA.
• netflix_titles.csv: Dataset used for analysis.
• README.md: Documentation providing an overview of the project, instructions, and interpretations of visualizations.

Getting Started

Installation

git clone https://github.com/sahasCodes/Exploratory-Data-Analysis-on-Netflix-Content

Prerequisites

Ensure you have this libraries are installed:

pip install -r requirements.txt

Code Overview

• Importing Libraries: Code to import necessary libraries.
• Importing Dataset: Code to import the Netflix dataset.
• Basic Statistics and Information: Displaying basic statistics and information about the dataset.
• Data Cleaning: Handling null values and renaming columns.
• Exploratory Data Analysis (EDA): Visualizations of Netflix content trends.
• Question and Answer: Visualizations to answer specific questions about the data.

Question Explored

1. Proportionality between the Genre of Movies and TV Shows: Visualizations showing the distribution of genres for both movies and TV shows.
2. Top 10 Actors on Netflix: Identifying and visualizing the top 10 actors based on the total number of content they are associated with.
3. Movies vs TV Shows Trend Over the Years: A line chart illustrating the historical trends in the release of TV shows and movies on Netflix.

Interpretations

• The visualizations offer insights into the diversity of genres, top actors, and historical trends in the Netflix content library. *The project provides a foundation for further analysis and understanding of the Netflix dataset. Feel free to explore the Jupyter Notebook to gain deeper insights into the Netflix dataset. Contributions, issues, and feedback are welcome!