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Analyzing Content Arrival Trend on Netflix data provided by Kaggle

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Netflix-Data-Analysis

Table of Contents

  1. Installation
  2. Project Motivation
  3. File Description
  4. Results
  5. Licensing, Authors, and Acknowledgements

Installation

Dependencies

  • Python >=3.7
  • matplotlib >= 3.4.2
  • numpy >= 1.21.1
  • pandas >= 1.3.1
  • seaborn >= 0.11.1

User Installation

Assuming you already have Python>3.7 up and running, the easiest way to install the requirements is using pip or conda. This tutorial assumes using pip for the installation purposes.

Create a virtual environment to set up the project. (Optional Step)

python3 -m venv <my_env>

cd to the root directory of the code and install the requirements using

pip install -r requirements.txt

Project Motivation

For this project, I was interested in Netflix data available as of 2021. The questions of interest that we will be exploring are:

  • In what month of the year, does the most new content gets added to netflix?
  • What is the average delay between release of a show and its arrival on netflix.
  • How does the different genres relate to the target audience belonging to different age groups?
  • Which Country has most content available on netflix?

File Description

For the purpose of answering above questions, this project contains a single notebook that addresses the questions. The data under consideration is available as a csv file which is also publicly available as Netflix Movies and TV Shows on Kaggle.

Results

The findings of the analysis are discussed in the jupyter notebook as well as in a blog post here.

Licensing, Authors, and Acknowledgements

All data credit to Netflix for data. You can find the Licensing for the data and other descriptive information on Kaggle at Netflix Movies and TV Shows.

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