Machine Learning Classification Problem
| Objective : Designing an optimal and efficient model that can help the bank identify the potential customers who have a higher probability of purchasing the loan which will increase the success ratio while at the same time reduce the cost of the campaign
| About this dataset : This case is about a bank (Thera Bank) which 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 to better target marketing to increase the success ratio with a minimal budget.
The department wants to build a model that will help them identify the potential customers who have a higher probability of purchasing the loan. This will increase the success ratio while at the same time reduce the cost of the campaign.
| Read This Before Reading the Project: I use my own custom made module to improve code readability in the Jupyter Notebook. If you need to see the functions which are imported from santa_modelling, please see the santa_modelling.py file in the same repository
Project done by : Santo K. Thomas | santokalayil@gmail.com | +91 8891960880