Customer Analysis and Segmentation using KMeans and Logistic Regression. This is a sample project made for customer Analysis, the dataset used is Mall_Customer.csv and for additional feature that is creditcard worthiness is being also calculated by creating a new column called csw(CreditScore Worhhiness) which will be the target for Logistic Regression analysis based on the Age , Gender , Annual income and Spending Score. The Current Project Divides the 200 Customers into 5 clusters based on their Annual Income and Spending Score. The Spending Score is a score or value provided by the mall based on the customer's purchasing. The Feature of Adding a Credit score or Credit worthiness will help the Mall administrators in providing the needed facilities to boost the purchasing power and sales of their mall or can be provided to banks so that the may approch the needed members for a credit card option there by boosting the banking economy. If the data is analyased from the K-Means Clustering we find that there is group of Impulsive buyers with less income and high spending score which is key for banks and credit givers.