This data visualization was created during Execute Big's Tech Roulette Week 3, in which I learned the basics of data visualization. Using Python Pyplot and a dataset that includes information about all TV shows and movies available on Netflix as of 2019, I created 3 visualizations to help determine what types of shows/movies Netflix should create/add to make the most money.
Data visualization is a powerful tool that can make complicated and large amounts of data appealing and understandable to non-technical people by portraying overall trends and patterns through graphs.
The overall goal is to determine what types of shows/movies Netflix should create. By answering the following smaller questions, I will be more informed and able to answer this overarching goal.
This will help me figure out who to hire as a director of new shows/movies.
In this visualization, directors are considered better if they have directed more shows on Netflix. Only the top 5 directors are shown.
This will determine the ratio of shows to movies Netflix should make.
In this visualization, popularity is based on count. For instance, if there are 30 movies and 25 shows, movies are more popular.
This can help me determine if there's any shows/movies from certain time periods that Netflix may want to expand upon.
Based on the three visualizations created, Netflix should prioritize movies over shows because movies are more popular. When creating new movies/shows, Netflix should hire Raúl Campos, Jan Suter, Marcus Raboy, Jay Karas, Cathy Garcia-Molina, and Jay Chapman. When adding existing movies/shows, Netflix should add older movies/shows to add more variety of movies/shows from earlier time periods, which will make Netflix more appealing to those who enjoy older movies/shows.