https://github.com/minimal-scouser/music-recommendation-system
A jupyter notebook with music recommendation script
https://github.com/minimal-scouser/music-recommendation-system
machine-learning pandas python
Last synced: 12 days ago
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A jupyter notebook with music recommendation script
- Host: GitHub
- URL: https://github.com/minimal-scouser/music-recommendation-system
- Owner: minimal-scouser
- Created: 2018-06-18T13:17:11.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2018-06-20T06:41:18.000Z (about 7 years ago)
- Last Synced: 2025-03-14T04:41:45.945Z (4 months ago)
- Topics: machine-learning, pandas, python
- Language: Jupyter Notebook
- Homepage:
- Size: 2.83 MB
- Stars: 2
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Music Recommendation System
Basing on the interests of the listener this Unsupervised Machine Learning model recommends music tracks to the user. The main crietrion to know or to draw an assumption on what the user likes are the factors such as his most listened list of tracks' metadata: artist, genre, world-wide reception, popularity and characteristics of the track like temp, bass, acoustiness and many more.
The data used in the project are exported from the [FMA dataset](https://github.com/mdeff/fma) available on GitHub.
## Getting Started
You need to have the following softwares installed inorder to run this file.
* [Ananconda](https://conda.io/docs/user-guide/install/index.html)
### Installation
Follow the above provided link for installing the software.
Once you've installed [Ananconda](https://conda.io/docs/user-guide/install/index.html) you should now be able to open [Jupyter Notebook](http://jupyter.org/) which you can find in your PC. Then open the file downloaded from the repository and make sure that the datasets and the file are in the same directory.
You can now start creating your own model or tweaking the present one.
### Collaboration
This was done in collaboration with [Sarath](github.com/sarathisme). Thanks bro.
## Inspiration
A quick shoutout to Mr.Venkatesh, Ms.Keerthi and Mr.Goutham.
Thank You!