Problem 1: Clustering

A leading bank wants to develop a customer segmentation to give promotional offers to its customers. They collected a sample that summarizes the activities of users during the past few months. You are given the task to identify the segments based on credit card usage.

1.1Read the data and do exploratory data analysis. Describe the data briefly. - 5 points

1.2Do you think scaling is necessary for clustering in this case? Justify. - 5 points

1.3Apply hierarchical clustering to scaled data. Identify the number of optimum clusters using Dendrogram and briefly describe them - 7.5 points

1.4Apply K-Means clustering on scaled data and determine optimum clusters. - 7.5 points

1.5Describe cluster profiles for the clusters defined. Recommend different promotional strategies for different clusters. - 5 points

Data Dictionary for Market Segmentation:

1. spending: Amount spent by the customer per month (in 1000s) 2. advance_payments: Amount paid by the customer in advance by cash (in 100s) 3. probability_of_full_payment: Probability of payment done in full by the customer to the bank 4. current_balance: Balance amount left in the account to make purchases (in 1000s) 5. credit_limit: Limit of the amount in credit card (10000s) 6. min_payment_amt : minimum paid by the customer while making payments for purchases made

monthly (in 100s) 7. max_spent_in_single_shopping: Maximum amount spent in one purchase (in 1000s)

The data to be analyzed can be downloaded from the Google Drive using the link:

https://docs.google.com/spreadsheets/d/1W248C8VFKmJlR6cFfK2e4ABHLdkRLwxQmd4fWVZ4O4k/edit?usp=sharing