Using Data to Analyze Netflix: Are You Still Watching?

Posted on Jun 13, 2021

The skills the author demonstrated here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Github| LinkedIn

Data Science Introduction

For this project, I decided to analyze a data set of Netflix Movie and TV show Titles to see what common trends have occurred over the years. Netflix is one of the biggest video streaming services on the planet with over 7,787 pieces of content on their platform at the time of this data set's collection. With its start as a DVD rental platform, Netflix has risen to make a huge name for itself announcing in 2019 that they had signed on 135 million paid customers worldwide.

A huge factor to their success is the underlying use of Big Data. By gathering information from practically every customer interaction, Netflix is able to get access to the minds of their viewers and recommend what they would like to watch next before they even finish their current movie or show. In this analysis, I wanted to examine the addition of content to Netflix in search for trends and similarities from all regions of Netflix' reach over the world.

Data Set

This data set was acquired from Kaggle and included many different variables to analyze from. Along with the titles of movies and tv shows, some of the other inputs were: Director, Cast, Date Added, Release Year, Description, Duration, etc. Using these variables I was able to create visualizations for a number of different observations.

Data Analysis

Title Count

Using Data to Analyze Netflix: Are You Still Watching?

Time Series

Using Data to Analyze Netflix: Are You Still Watching?

From these charts, we can see that movie titles are more dominant on Netflix over tv shows. As we approach 2020 though, movie titles come down and tv shows spike up. Why might this be? One plausible explanation could be the pandemic that entered our doorsteps at the start of 2020. Another reason could be the prevalence of Netflix Original Series. With the expansion of tv shows produced by Netflix demand for movies could be in recession.

Content Added Per Month

Using Data to Analyze Netflix: Are You Still Watching?

Using Data to Analyze Netflix: Are You Still Watching?

These two charts show us the number of content added every month from 2010 to 2020 as well as the amount of titles add per month on average. As you can see, February has the least amount of content added, where as October has the most.

Country Overview

Using Data to Analyze Netflix: Are You Still Watching?

Using Data to Analyze Netflix: Are You Still Watching?

United States holds the top spot for most content available on Netflix. Although that's true, it's interesting to note that International Movies and TV shows seem to dominate by genre. What you don't see in these charts are the other countries outside the top 15. This is a probable reason behind this occurrence.

Duration Overview

Using Data to Analyze Netflix: Are You Still Watching?

Using Data to Analyze Netflix: Are You Still Watching?

In this box plot, we can see the duration of a movie plays out in each of the top 11 countries. Whats interesting to note is the decline in duration on average over the years, from 119 minutes in 2001 to 90 minutes in 2020.

Rating & Cast Recurrence

Using Data to Analyze Netflix: Are You Still Watching?

Using Data to Analyze Netflix: Are You Still Watching?

In these charts we see population of titles by rating and cast recurrence by titles. R rated movies and Bollywood actors seem to be in high demand.


Using Data to Analyze Netflix: Are You Still Watching?

A word cloud of the most recurring words in Netflix Titles.


There are numerous ways we can see how big data analysis plays a role in the way Netflix programs its user experience. By continuing to collect this data from users, Netflix can continue to find success as it grows bigger each year. Netflix is a worldwide business. Knowing what to add to whichever region of the globe is crucial in the way Netflix continues to grow its user base. With the continued updating of this data set, we can see the rising and falling trends that happen every so often as well as in what region and by doing so we can witness the awe that is the success of Netflix.

About Author

Aditya Jayasuri

Aditya is a recent Data Science graduate at NYC Data Science Academy with hopes of paving a new pathway in his career. A graduate from Drexel University with a B.S. in Entertainment & Arts Management previous experience includes...
View all posts by Aditya Jayasuri >

Related Articles

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI