Competition - Dance Party Songs

Which songs are most suitable for a dancing party?

📖 Background

It's that vibrant time of year again - Summer has arrived (for those of us in the Northern Hemisphere at least)! There's an energy in the air that inspires us to get up and move. In sync with this exuberance, your company has decided to host a dance party to celebrate. And you, with your unique blend of creativity and analytical expertise, have been entrusted with the crucial task of curating a dance-themed playlist that will set the perfect mood for this electrifying night. The question then arises - How can you identify the songs that would make the attendees dance their hearts out? This is where your coding skills come into play.

💾 The Data

You have assembled information on more than 125 genres of Spotify music tracks in a file called spotify.csv, with each genre containing approximately 1000 tracks. All tracks, from all time, have been taken into account without any time period limitations. However, the data collection was concluded in October 2022. Each row represents a track that has some audio features associated with it.

Column	Description
`track_id`	The Spotify ID number of the track.
`artists`	Names of the artists who performed the track, separated by a `;` if there's more than one.
`album_name`	The name of the album that includes the track.
`track_name`	The name of the track.
`popularity`	Numerical value ranges from `0` to `100`, with `100` being the highest popularity. This is calculated based on the number of times the track has been played recently, with more recent plays contributing more to the score. Duplicate tracks are scored independently.
`duration_ms`	The length of the track, measured in milliseconds.
`explicit`	Indicates whether the track contains explicit lyrics. `true` means it does, `false` means it does not or it's unknown.
`danceability`	A score ranges between `0.0` and `1.0` that represents the track's suitability for dancing. This is calculated by algorithm and is determined by factors like tempo, rhythm stability, beat strength, and regularity.
`energy`	A score ranges between `0.0` and `1.0` indicating the track's intensity and activity level. Energetic tracks tend to be fast, loud, and noisy.
`key`	The key the track is in. Integers map to pitches using standard Pitch class notation. E.g.`0 = C`, `1 = C♯/D♭`, `2 = D`, and so on. If no key was detected, the value is `-1`.
`loudness`	The overall loudness, measured in decibels (dB).
`mode`	The modality of a track, represented as `1` for major and `0` for minor.
`speechiness`	Measures the amount of spoken words in a track. A value close to `1.0` denotes speech-based content, while `0.33` to `0.66` indicates a mix of speech and music like rap. Values below `0.33` are usually music and non-speech tracks.
`acousticness`	A confidence measure ranges from `0.0` to `1.0`, with `1.0` representing the highest confidence that the track is acoustic.
`instrumentalness`	Instrumentalness estimates the likelihood of a track being instrumental. Non-lyrical sounds such as "ooh" and "aah" are considered instrumental, whereas rap or spoken word tracks are classified as "vocal". A value closer to `1.0` indicates a higher probability that the track lacks vocal content.
`liveness`	A measure of the probability that the track was performed live. Scores above `0.8` indicate a high likelihood of the track being live.
`valence`	A score from `0.0` to `1.0` representing the track's positiveness. High scores suggest a more positive or happier track.
`tempo`	The track's estimated tempo, measured in beats per minute (BPM).
`time_signature`	An estimate of the track's time signature (meter), which is a notational convention to specify how many beats are in each bar (or measure). The time signature ranges from `3` to `7` indicating time signatures of `3/4`, to `7/4`.
`track_genre`	The genre of the track.

Source (data has been modified)

Data manipulation

Inspection of the dataset presented some minor issues to deal with.

When looking at the dataset initially, I noticed that the album and track name were included. This is likely to cause issues when generating dummy variables, as it would create a large number of features for variables that are likely to have little to no predictive power. I therefore decoded to drop this from our data.

The second was a column with a missing value from the artists column, given it was a single variable I decided the best action was to drop this row from our data.

After this was completed, we needed to one-hot encode the categorical columns in our dataset. This is necessary otherwise the

# Basic import 
import pandas as pd

# Import of the relevant data
spotify = pd.read_csv('data/spotify.csv')

# Data inspection
spotify.head()
spotify.info()

# Drop album and track name columns
spotify = spotify.drop(['album_name', 'track_name'], axis=1)
spotify = spotify.dropna()

# For categorical columns generation of dummy variables
cols_to_encode = ['artists', 'explicit', 'key', 'mode', 'track_genre']
df = pd.get_dummies(spotify[cols_to_encode], drop_first=True)  # Drop first to avoid multicollinearity

When looking at the data, we can see there is no missing values. We also note that there are categorical columns that will need to be encoded for any machine learning analysis, we notice these are track_genre, artists, album_name and track_name.

It is clear that encoding the album and track name will provide little to no insight, this would generate a large number of dummies with small sample sizes.

💪 Challenge

Your task is to devise an analytically-backed, dance-themed playlist for the company's summer party. Your choices must be justified with a comprehensive report explaining your methodology and reasoning. Below are some suggestions on how you might want to start curating the playlist:

Use descriptive statistics and data visualization techniques to explore the audio features and understand their relationships.
Develop and apply a machine learning model that predicts a song's danceability.
Interpret the model outcomes and utilize your data-driven insights to curate your ultimate dance party playlist of the top 50 songs according to your model.

✅ Checklist before publishing into the competition

Rename your workspace to make it descriptive of your work. N.B. you should leave the notebook name as notebook.ipynb.
Remove redundant cells like the judging criteria, so the workbook is focused on your story.
Make sure the workbook reads well and explains how you found your insights.
Try to include an executive summary of your recommendations at the beginning.
Check that all the cells run without error.