Customer Segmentation

Customer segmentation is the practice of separating customers into groups that reflect similarities among customers in each cluster. I will divide customers into segments to optimize the significance of each customer to the business.

Dataset Source

The Dataset was collected from Kaggle.

Data Pre-processing and EDA

1. 'Avg Cred Limit' and Total visits online had outliers and were removed using IQR.

2. Avg_Credit_Limit is skewed

3. Each customer has an average of 5 credit cards, visits bank twice a both online and in person, also makes 2 calls to the bank

4. Average Credit Limit is proportional to the number of credit cards

5. Customers with higher credit limit on credit cards make lesser calls

6. Customers who makes calls to the bank prefer visiting the bank in person rather than online.

7. Loyal Customers have a credit limit higher than around 60k

K-means clustering with elbow method

Finding the number of clusters and data labels for a given value of K is essential to the K-Means algorithm. The ideal number of clusters for K-means clustering is chosen using the elbow approach. The value of the cost function generated by various K values is plotted using the elbow approach. We should cease further clustering the data at the elbow, or the value of K at which improvement in distortion drops the most. For this problem, K should be set to 3.