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.

1 hidden cell

library(dplyr)
#install.packages("corrplot")
library(corrplot)
library(tidymodels)
library(ggplot2)

spotify <- readr::read_csv('data/spotify.csv', show_col_types = FALSE)
spotify

💪 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.

🧑‍⚖️ Judging criteria

CATEGORY	WEIGHTING	DETAILS
Recommendations	35%	Clarity of recommendations - how clear and well presented the recommendation is. Quality of recommendations - are appropriate analytical techniques used & are the conclusions valid? Number of relevant insights found for the target audience.
Storytelling	35%	How well the data and insights are connected to the recommendation. How the narrative and whole report connects together. Balancing making the report in-depth enough but also concise.
Visualizations	20%	Appropriateness of visualization used. Clarity of insight from visualization.
Votes	10%	Up voting - most upvoted entries get the most points.

✅ 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.

⌛️ Time is ticking. Good luck!

party <- spotify %>%
  dplyr::mutate_if(is.character,factor)

ggplot(data = party, aes(x = artists, fill = album_name)) +
  geom_bar(position = "dodge", stat = "count")