11/11/2023 0 Comments Spotify palette meaning![]() The data set has 32833 rows of observations with 23 variables. Loading Data spotify <- read.csv("spotify_songs.csv", stringsAsFactors=FALSE) GridExtra : Helps in arranging multiple plots on a gridįactoextra : Factoextra is usually used to visualize the output of multivariate data analysis, but in this project I have used it to plot the clusters of K-means algorithm.įpc : Provides various methods for clustering and cluster validation Viridis : Similar to Rcolorbrewer, helps with color palettes and other cosmetic purposes RColorBrewer : Provides multiple color palettes to be used in conjunction with GGplot visualisations ggplot2 provides elegant visualizations, that help to present insights in a delightful manner.tidyr helps in tidying data with dropna, fillna functions, extracting values from strings and thereby making the data more readable, concrete and complete.dplyr provides functions for data manipulation such as - adds new variables that are functions of existing variables, select, rename data, filter, summarise etc.Tidyverse : Tidyverse provides a collection of packages including “dplyr”, “tidyr”, “ggplot2” explained below Ggally : To plot the correlation analysis of variables in matrice form Kablextra : In addition to the kable function, kableextra library provides formatting functions which controls width etc.ĭT : Helps in presenting tables in a clean format, and has the ability to provide filters The kable function particularly helps in presenting tables, manipulating table styles Knitr : Helps display better outputs without any intense coding. Songs with similar characteristics are grouped into clusters by the algorithm and these clusters help in understanding the audio attributes of the popular songs.To understand the popularity of songs I used K-means clustering method to group clusters and identified how far the clusters are from each other.Chi square testing for categorical variables.Statistical Testing to understand variable behaviors :.Visualization techniques that uncover patterns and insights about the audio features and their behaviour with each other.The idea is to help an end user to gain better understanding of what goes behind the most popular songs on Spotify. This analysis aims to provide an understading on which songs / genres are the most popular ones. Understand how the audio features perform across clusters and thereby on the songs.What are the audio features/attributes of these clusters?.Which genres are popular by the clusters - i.e are they pop? are they rap?.Build a K -means model to identify the most popular songs by each cluster:.The idea behind the project is to use this dataset to : This particular data set, built via the spotifyr package has details of track names, artists, types of genres, sub genres and other audio features. It suggests music based on your frequently liked songs/artists. Spotify as a music application does a very good job in recommeding music to its users. ![]() ![]() This dataset is extracted using the spotifyr package and was obtained from rfordatascience github. ![]()
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