The document outlines a music recommender system developed by a team using a dataset sourced from Kaggle via the Spotipy API, which includes various CSV files related to music tracks. It describes the implementation of several clustering algorithms, such as KMeans, Birch, and Gaussian Mixture Model, for building the recommendation model. Links to supplementary resources, including GitHub and Medium articles, are also provided.

1. MUSIC
RECOMMENDER
SYSTEM
10220017 - Shereva Miranda
10220061 - Darryl Andhika Moniaga
10220070 - Audrey Lynn Johanes

2. DATASET
Source
Raw dataset is taken from Kaggle (Spotipy
API):
https://www.kaggle.com/code/vatsalmavani/
music-recommendation-system-using-spotify-
dataset/input
Content
The dataset is a CSV File containing
tracks.csv, data.csv, data_by_genre.csv, dan
data_by_year.csv

3. IMPORTING MODULES

4. IMPORTING DATASET

5. DATA INFORMATION

6. DATA INFORMATION

7. CLUSTERING ALGORITHM

8. CLUSTERING ALGORITHM
STANDARD SCALER

9. CLUSTERING ALGORITHM
PRINCIPAL COMPONENT ANALYSIS (PCA)

10. CLUSTERING ALGORITHM
IMPORT METRIC LIBRARY

11. CLUSTERING ALGORITHM
KMEANS CLUSTERING

12. CLUSTERING ALGORITHM
KMEANS CLUSTERING

13. CLUSTERING ALGORITHM
BIRCH CLUSTERING

14. CLUSTERING ALGORITHM
MINI-BATCH KMEANS CLUSTERING

15. CLUSTERING ALGORITHM
FUZZY C MEAN CLUSTERING

16. CLUSTERING ALGORITHM
GAUSSIAN MIXTURE MODEL

17. ACCURACY
The highest index is shown from the model KMeans Clustering

18. MODEL BUILDING

19. MODEL BUILDING

20. MODEL BUILDING

21. MODEL BUILDING

22. MODEL BUILDING

23. https://gist.github.com/shemrnd/899e3eb0e7a0ada1abc04910111d974d
GITHUB
https://medium.com/@shemrnd/music-recommendation-system-6f8ccf4c6ece
MEDIUM