Netflix Data Analysis

Netflix has become a major player in the entertainment industry offering a diverse array of TV Shows and Movies to its global audience.

This dataset provides a snapshot of Netflix's content library including various attributes such as titles, genres, release years, ratings and durations.

Dataset

The dataset used for this analysis is sourced from Kaggle and includes information on Netflix TV Shows and Movies.

Link to the Dataset : Netflix TV Shows and Movies

Problem Statement

• To gain insights into the content available on Netflix, understanding the patterns and trends to uncover valuable insights into how the platform curates and evolves its offerings.

• This Exploratory Data Analysis (EDA) aims to address the following key questions :

  • Content Distribution : What are the distribution patterns of TV shows and movies across different genres and countries? How does the content vary in terms of release year and rating?

  • Trend Analysis : Are there observable trends in the release of TV shows and movies over time? How has the number of new additions to Netflix's library evolved?

  • Genre Popularity : What are the most and least popular genres in Netflix’s library? How does genre popularity differ by region or over time?

  • Content Characteristics : What are the typical characteristics (e.g., duration, rating) of TV shows vs movies? How do these characteristics vary by genre or release year?

  • Regional Diversity : How diverse is Netflix’s content offering in terms of geographic origin? Are there particular regions that contribute more significantly to Netflix’s library?

• This analysis will provide a deeper understanding of Netflix content library, revealing trends and patterns that can address future content strategy and development.

Table of Contents

• Setting up the Enviroment
• Libraries required for the Project
• Getting started with Repository
• Steps involved in the Project
• Conclusion
• Link to the Notebook

Setting up the Enviroment

Jupyter Notebook is required for this project and you can install and set it up in the terminal.

• Install the Notebook

pip install notebook

• Run the Notebook

jupyter notebook

Libraries required for the Project

Pandas

• Go to Terminal and run this Code

pip install pandas

• Go to Jupyter Notebook and run this Code from a Cell

!pip install pandas

Matplotlib

• Go to Terminal and run this Code

pip install matplotlib

• Go to Jupyter Notebook and run this Code from a Cell

!pip install matplotlib

Seaborn

• Go to Terminal and run this Code

pip install seaborn

• Go to Jupyter Notebook and run this Code from a Cell

!pip install seaborn

Getting Started

• Clone the repository to your local machine using the following command :

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

Steps involved in the Project

Importing Libraries

• Importing Pandas, Matplotlib and Seaborn Libraries

Reading CSV File

• Reading CSV File by using pd.read_csv() function

Overview of the Dataset

• Information about shape and size of the Dataset

• Columns present in the Dataset

• Info about the Dataset

Handling Null values in the Dataset

• Filling the Null values with most frequent category in the Columns

Changing DataType of Columns

• Modifying the DataType of date_added column to Pandas DateTime Format

Utilizing existing information to create new Columns

• Extracting Year, Month and Dates from date_added Column

• Splitting listed_in column and selecting first value as Genre

• Splitting cast column and selecting first value as Lead Actor

Splitting the Dataset

• Splitting the Dataset based on type of Content, i.e TV Shows and Movies

Statistical Analysis

• No. of TV Shows and Movies available on Netflix

• No. of shows in each Rating Category

• No. of shows released each Year, etc.

Data Visualization

• No. of TV Shows and Movies available on Netflix

• No. of shows in each Rating Category

• No. of shows uploaded on Netflix each Year

• No. of shows uploaded on Netflix each Month

• No. of shows uploaded on Netflix each Day

• No. of shows available on Netflix in each Country

• No. of Movies released on Netflix in each Genre

• No. of TV Shows released on Netflix in each Genre

• No. of Movies for a Lead Actor on Netflix

• No. of TV Shows for a Lead Actor on Netflix

• Avg. Length of Movies in each Genre

• Avg. Length of TV Shows in each Genre

• Distribution of Length of Movies on Netflix

• Distribution of Seasons of TV Shows on Netflix

Conclusion

• In summary, this analysis has provided valuable insights into Netflix’s content library, revealing trends and patterns that can inform strategic decisions.

• By addressing these insights, Netflix can enhance its content offerings and better meet the needs of its global audience.

Link to the Notebook

Netflix Data Analysis

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