In this project, we are going to implement customer segmentation based on credit card usage behavior with two different approaches (K-means and Hierarchical Clustering) The data contains the following columns: CUST_ID : Identification of credit card holder (Categorical) BALANCE : Balance amount left in their account to make purchases BALANCE_FREQUENCY : How frequently the Balance is updated, score between 0 and 1 (1 = frequently updated, 0 = not frequently updated) PURCHASES : Amount of purchases made from account ONEOFF_PURCHASES : Maximum …
You have a supermarket mall and through membership cards, you have some basic data about your customers like Customer ID, age, gender, annual income and spending score. Spending Score is something you assign to the customer based on your defined parameters like customer behavior and purchasing data. By the end of this case study, you would be able to answer the below questions. 1- How to achieve customer segmentation using Machine Learning algorithm (Hierarchical Clustering) …