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Info about the related issue (Aim of the project) : The main goal of this project is to develop a machine learning model that can recommend the music recommendation system based on the user's listening history.
Name: Sitam Meur
GitHub ID: sitamgithub-MSIT
Idenitfy yourself: JWOC 2024 Participant
Closes: #589
Describe the add-ons or changes you've made π
The solution is implemented with technologies like Scikit-learn, Scipy, Spotipy, etc.
I used Kaggle datasets for training purposes.
Performed the necessary data pre-processing and exploratory data analysis.
Trained the model K-means clustering and dimensionality reduction with PCA and t-SNE.
I checked the trained model with euclidean distance, spatial distance, and other similarity metrics.
At the end, model prediction is done using Spotipy (the Python library for the Spotify Web API).
Type of change βοΈ
What sort of change have you made:
[ ] Bug fix (non-breaking change which fixes an issue)
[x] New feature (non-breaking change which adds functionality)
[ ] Code style update (formatting, local variables)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] This change requires a documentation update
How Has This Been Tested? βοΈ
The changes that I have made have been thoroughly tested in my local VS code as well as the Google collab. I also added proper requirements in the requirements.txt file for reproducibility on other machines too. Also, model graphs are added, and model prediction is added to prove the working of the code.
Checklist: βοΈ
[x] My code follows the guidelines of this project.
[x] I have performed a self-review of my own code.
[x] I have commented my code, particularly wherever it was hard to understand.
[x] I have made corresponding changes to the documentation.
[x] My changes generate no new warnings.
[x] I have added things that prove my fix is effective or that my feature works.
[x] Any dependent changes have been merged and published in downstream modules.
Pull Request for ML-Crate π‘
Issue Title: Music Recommender System
Closes: #589
Describe the add-ons or changes you've made π
The solution is implemented with technologies like Scikit-learn, Scipy, Spotipy, etc. I used Kaggle datasets for training purposes. Performed the necessary data pre-processing and exploratory data analysis. Trained the model K-means clustering and dimensionality reduction with PCA and t-SNE. I checked the trained model with euclidean distance, spatial distance, and other similarity metrics. At the end, model prediction is done using Spotipy (the Python library for the Spotify Web API).
Type of change βοΈ
What sort of change have you made:
How Has This Been Tested? βοΈ
The changes that I have made have been thoroughly tested in my local VS code as well as the Google collab. I also added proper requirements in the requirements.txt file for reproducibility on other machines too. Also, model graphs are added, and model prediction is added to prove the working of the code.
Checklist: βοΈ