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Business Context - Banks are primarily known for money lending business. The more money they lend to people whom they can get good interest with timely repayment, the more revenue is for the banks. This not only save banks money from having bad loans but also improves image in the public figure and among the regulatory bodies.
The better the banks can identify people who are likely to miss their repayment charges, the more in advance they can take purposeful actions whether to remind them in person or take some strict action to avoid delinquency.
In cases where a borrower is not paying monthly charges when credit is issued against some monetary thing, two terms are frequently used which are delinquent and default.
Delinquent in general is a slightly mild term where a borrower is not repaying charges and is behind by certain months whereas Default is a term where a borrower has not been able to pay charges and is behind for a long period of months and is unlikely to repay the charges.
This case study is about identifying the borrowers who are likely to default in the next two years with serious delinquency of having delinquent more than 3 months.
We have a general profile about the borrower such as age, Monthly Income, Dependents and the historical data such as what is the Debt Ratio, what ratio of amount is owed with respect to credit limit, and the no of times defaulted in the past one, two, three months.
We will be using all these features to predict whether the borrower is likely to default in the next 2 years or not having delinquency of more than 3 months.
These kinds of predictions will help banks to take necessary actions.
Objective: Building a model using the inputs/attributes which are general profile and historical records of a borrower to predict whether one is likely to have serious delinquency in the next 2 years
We will be using Python as a tool to perform all kind of operations.
Main Libraries used
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