Objective : A bank was rolling out a new term deposits product and wanted to predict which of their existing customers to
target as part of maximizing ROI
Solution : Deployed Logistic regression to create a propensity model in R language to predict those customers most likely to
respond positively to the new product and the campaign
Key Achievement : Achieved a model accuracy of 80% and AUC of 90%

A bank was rolling out a new term deposits product and wanted to predict which of their existing customers to target as part of maximizing ROI. Deployed Logistic regression to create a propensity model in R language to predict those customers most likely to respond positively to the new product and the campaign. Achieved a model accuracy of 80 and AUC of 90.

Part backorders is a common supply chain problem and wanted to identify parts at risk of backorder before the event occurs so the business has time to react. Deployed random forest to predict whether the product actually went on backorder or not. Concluded a reduction in average waiting time from 10 days to 3 days.