Solve a problem

The data provides historical customer transactions for a retailer, for purchases made on their
e-commerce platform between Dec 2010 to Dec 2011. Customers are uniquely identified by
their Customer ID (Col G).

Problem Statement
Assume that the company has been experiencing high customer churn and a reduction in
repeat customers. As part of this case study, you are required to build a predictive model to
predict the likelihood of a customer churning

Churn Definition (Target Variable definition)
For this problem, the churn should be defined as follows:
The training data should include all customers who have made a transaction between 1
st Dec
2010 to 31st Aug 2011. Customers from this set who have NOT made any subsequent purchase
in the period Sep 2011 to Dec 2011 should be labeled as “Churn”. All other customers with
purchases made during this follow-up period should be labelled as “Not Churn”

Submission Guidelines
Applicants should submit a Jupyter Notebook with the case study solution. The solution
should incorporate:
• Target variable creation (based on churn definition provided)
• Customer level feature creation, from transaction dataset
• EDA on the features created
• Model training, validation and evaluation