The Allbank bank dataset was used to build a model that will help the marketing department to identify the potential customers who have a higher probability of purchasing the loan.
Logistic regression & multicollinearity, finding optimal threshold using AUC-ROC curve, Decision trees & pruning, misclassification analysis
AllLife Bank has a growing customer base. Majority of these customers are liability customers (depositors) with varying size of deposits. The number of customers who are also borrowers (asset customers) is quite small, and the bank is interested in expanding this base rapidly to bring in more loan business and in the process, earn more through the interest on loans. In particular, the management wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors).
A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise campaigns with better target marketing to increase the success ratio with a minimal budget.
You as a Data scientist at AllLife bank has to build a model that will help marketing department to identify the potential customers who have higher probability of purchasing the loan. This will increase the success ratio while at the same time reduce the cost of the campaign.
- To predict whether a liability customer will buy a personal loan or not.
- Which variables are most significant.
- Which segment of customers should be targeted more.