When I was in college I had developed a project with jupyter notebook, which consumes data from the Netflix Prime Video Movies and TV Shows set. The idea was to use this set of data, clean, analyze and develop a stage where I could recommend movies and TV shows.

I was very happy with the result. But I wanted more, I wanted to take this notebook and transfer it to an application where I could interact with the project. So create a personal project where I can use what I studied and learned over time.

But something was missing, which was how am I going to show this result of my project. During that time I discovered this tool Streamlit, ohhhhhhhhhh!!!!! Incredible !!! The flexibility I gained using it was very good and in addition to being able to deploy using their platform, this way I can show what I did.

I want to thank Kaggle - @shivamb, for making the sets below available. In addition to the Netflix set, there are 3 more.

• Netflix Movies and TV Shows
• Hulu Movies and TV Shows
• Disney+ Movies and TV Shows
• Amazon Prime Movies and TV Shows

From these 4 sets, the idea of creating a single one came up to be able to expand the data further, to be able to create more recommendations. Follow the link below.

4 Services Streaming Movies and Tv Shows

If you want to understand the process more, I have a post and 4 more notebooks where I explain the notebook I created.

• Post - K-Means Recommend Movies and Tv Shows
• Hulu Notebook
• Amazon Notebook
• Disney Notebook
• Notebook Netflix

You can check out the application I developed using Streamlit and using this data.

• Blueflix Streamlit
• Project

This dataset has two sets:

all_streaming.csv: This set has a unification of the 4 sets above, it received a column to identify each streaming, I also added the groups column where I used the k-means algorithm to unite the best groups.

all_gender.csv: This set has separation of all genres from the streaming sets. Each column being binary. Additionally, I added the groups column where I used the k-means algorithm to join the groups.

You can find the notebook for this step at this link:

• Clear data and Generate Dataset