Exploratory data analysis (EDA) is a task of analyzing data using simple tools from statistics, simple plotting tools. Every machine learning problem solving starts with EDA. It is probably one of the most important part of a machine learning project. With the growing market, the size of data is also growing. It becomes harder for companies to make decision without proper analyzing it.
Customer segmentation is the process by which you divide your customers up based on common characteristics – such as demographics or behaviours, so you can market to those customers more effectively. K-means is an unsupervised learning method for clustering data points. The algorithm iteratively divides data points into K clusters by minimizing the variance in each cluster.
This repository contains scripts or source code about EDA and customer segmentation of Starbucks using the K-Means algorithm. In this project, PCA was also carried out to reduce the dimension of data. In addition, by using the Elbow method, I was able to get four proportional clusters: diamond customers, gold customers, silver customers, and bronze customers.
Dataset source: https://www.kaggle.com/datasets/blacktile/starbucks-app-customer-reward-program-data