Unsupervised Anomaly Detection on Spotify data 🎵: K-Means vs Local Outlier Factor

Anomaly detection is a crucial in any Data Science project. It's an important component during
Exploratory Data Analysis and sometimes it's overlooked.

In this project I show the steps (methodology) that I followed for Anomaly Detection in my own personal
Spotify data (only song features). I implemented a characterisation of the dataset with K-Means algorithm
and parallely used Local Outlier Factor, to find the anomalies/outliers.

What's in this repository

Here you can find the notebook where the anomaly detection analysis is performed
(here)
and how I got the data (here)

Data Visualizations