Unsupervised Machine Learning

Goal

To evaluate whether Machine Learning can be used to automatise playlist creation.

Overview

Moosic is a small startup that creates playlists curated manually by music experts. Their listeners love the personal touch, which they achieve by capturing the "mood" or "vibe".

Board: Believes that they need at least a degree of automatisation, as music experts are not able to keep up with the demand. Currently the whole creation process is done manually.

Music Experts: Are skeptical that audio features on their own are not enough to capture the "mood" which is very subjective that only a human can judge.

Context

Moosic wants the data science team to use a dataset that has been collected from the Spotify API and contains the audio features (tempo, energy, danceability…) for a few thousand songs. After useing a basic clustering algorithm such as K-Means to divide the dataset into a few clusters the data team shall answer the following two questions:

1. Are Spotify’s audio features able to identify “similar songs”, as defined by humanly detectable criteria?
2. Is K-Means a good method to create playlists?

Task:

• Import list of 5000 songs collected from Spotify API
• Use basic clustering ex.: K-Means to divide dataset into clusters
• Validate clusters, export clusters (playlists) to Spotify and listen to some of the songs

Challenges:

• Difficult to evaluate the results without listening to each playlist
• No tangible way to measure accuracy
• Unevenly large clusters
• Subjective - what is a good playlist?

Solutions:

• Must be visualized, so we can see the overlaps and the outliers
• Limit the number of features to 3 (or multiples of 3) so it can be visualized in 3D scatterplot
• Find a balance between K-score and the business objectives
• Instead of replacing music experts, ML does the "heavy lifting" and they fine-tune the results

Approach

1. Evaluate the database; basic cleaning, ex.: missing, corrupted values, correct data types
2. Exploration of audio features
3. Decide which features to drop, and which features to use
4. K-Means clustering
5. Evaluation of clusters
6. Sub-clustering
7. Evaluation of final clusters

Deliverables

5 minute PowerPoint presentation found here to the Board of Directors, that summarizes the findings and suggests a course of action. Python code is found here.

Skills & Tools

1. Data Cleaning & Quality Assurance
2. Data Preprocessing: Scaling
3. K-Means Clustering
4. Elbow Method and Silhouette Score
5. Data Visualization (3D Scatterplot)