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https://github.com/cintia0528/data_science-unsupervised_machine_learning

I aim to automate playlist creation for Moosic, a startup known for manual curation, using Machine Learning, while addressing skepticism about the ability of audio features to capture playlist "mood."
https://github.com/cintia0528/data_science-unsupervised_machine_learning

data data-preprocessing data-scaling data-science data-visualization datacleaning elbow-method kclustering machine-learning pandas python silhouette-score unsupervised-machine-learning

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I aim to automate playlist creation for Moosic, a startup known for manual curation, using Machine Learning, while addressing skepticism about the ability of audio features to capture playlist "mood."

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# 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](https://drive.google.com/file/d/1vUTZUToQtD97X_53d7Ht7nSnJrvjJ5G_/view?usp=sharing) to the Board of Directors, that summarizes the findings and suggests a course of action.

**Python code** is found [here](4_0_5000_songs_FINAL_NOTEBOOK.ipynb).



## 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)