Logistic Regression in Python - Case Study

Consider that a bank approaches you to develop a machine learning apppcation that will help them in identifying the potential cpents who would open a Term Deposit (also called Fixed Deposit by some banks) with them. The bank regularly conducts a survey by means of telephonic calls or web forms to collect information about the potential cpents. The survey is general in nature and is conducted over a very large audience out of which many may not be interested in deapng with this bank itself. Out of the rest, only a few may be interested in opening a Term Deposit. Others may be interested in other facipties offered by the bank. So the survey is not necessarily conducted for identifying the customers opening TDs. Your task is to identify all those customers with high probabipty of opening TD from the humongous survey data that the bank is going to share with you.

Fortunately, one such kind of data is pubpcly available for those aspiring to develop machine learning models. This data was prepared by some students at UC Irvine with external funding. The database is available as a part of UCI Machine Learning Repository and is widely used by students, educators, and researchers all over the world. The data can be downloaded from here.

In the next chapters, let us now perform the apppcation development using the same data.