https://github.com/sc0v0ne/blueflix
Simple Application Recommend Movies and Tv Shows
https://github.com/sc0v0ne/blueflix
csv k-means k-means-clustering kaggle movies numpy pandas python recommends-movies sklearn streamlit
Last synced: 12 months ago
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Simple Application Recommend Movies and Tv Shows
- Host: GitHub
- URL: https://github.com/sc0v0ne/blueflix
- Owner: sc0v0ne
- Created: 2023-08-24T23:38:15.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-01-18T21:40:24.000Z (about 2 years ago)
- Last Synced: 2025-02-17T14:47:40.537Z (12 months ago)
- Topics: csv, k-means, k-means-clustering, kaggle, movies, numpy, pandas, python, recommends-movies, sklearn, streamlit
- Language: Python
- Homepage: https://blueflix.streamlit.app
- Size: 8.99 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 16
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Metadata Files:
- Readme: README.md
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README
# Blueflix Streamlit
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](https://www.kaggle.com/datasets/shivamb/netflix-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](https://streamlit.io), 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](https://www.kaggle.com/datasets/shivamb/netflix-shows)
- [Hulu Movies and TV Shows](https://www.kaggle.com/datasets/shivamb/hulu-movies-and-tv-shows)
- [Disney+ Movies and TV Shows](https://www.kaggle.com/datasets/shivamb/disney-movies-and-tv-shows)
- [Amazon Prime Movies and TV Shows](https://www.kaggle.com/datasets/shivamb/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](https://www.kaggle.com/datasets/sc0v1n0/4-services-streaming-movies-and-tv)
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 ](https://dev.to/sc0v0ne/k-means-recommend-movies-and-tv-shows-156m)
- [Hulu Notebook](https://www.kaggle.com/code/sc0v1n0/k-means-recommend-movies-and-tv-shows-hulu)
- [Amazon Notebook](https://www.kaggle.com/code/sc0v1n0/k-means-recommend-movies-and-tv-shows-amazon-prime)
- [Disney Notebook](https://www.kaggle.com/code/sc0v1n0/k-means-recommend-movies-and-tv-shows-disney)
- [Notebook Netflix](https://www.kaggle.com/code/sc0v1n0/k-means-recommend-movies-and-tv-shows-netflix)
## Conclusion
This personal project is a dream that I am developing, I want to evolve it further with the skills I acquire along the way. I won't always be adding updates, because I have ideas of other projects that I want to evolve, but I won't stop paying attention. I hope that other developers understand my codes and that I can transfer what I learned in this time. I hope you enjoyed it. Please, if you could leave a like on my post or on my notebooks, I would really appreciate it, so I can know if you liked it. Thank you for reading this far.