If you have read my previous article here, you will not be surprised about this article. I continued exploring K-pop data.
As I say before K-pop idols have data! And it will be interesting if we explore this. I analyzed each feature to make the data exploration easier. I used two datasets: K-pop boy group and K-pop girl group profiles from 1992 to 2020. I used pie charts to visualize data because I want to see the percentage of the totals data.
Data source : K-Pop Database
First of all, data preparation: importing needed packages (pandas and matplotlib) and datasets…
K-pop (abbreviation of Korean pop) is a genre of popular music originating in South Korea. K-pop is a term that is often used on the internet, and there is quite a popular fan following around the world. This ‘once local, now global’ phenomenon has an interesting background.
K-pop idols are groups and artists formed by the various entertainment companies creating catchy Korean popular music and targeting younger audiences. The music groups are formed from a group of people who are all particularly talented in at least one of the following: singing, rapping, and dancing. These idols often enter the entertainment…
I created a model that estimates credit card customer segmentation to help the company to define its marketing strategy. I used the K-means algorithm with the K value determined by silhouette score. I also used PCA for dimension reduction and better visualization.
Data source : Credit Card Dataset for Clustering
This case requires to develop of a customer segmentation to define marketing strategy. The sample Dataset summarizes the usage behavior of about 9000 active credit cardholders during the last 6 months…