The project involved developing a credit risk default model on Indian companies using the performance data of several companies to predict whether a company is going to default on upcoming loan payments.
Businesses or companies can fall prey to default if they are not able to keep up their debt obligations. Defaults will lead to a lower credit rating for the company which in turn reduces its chances of getting credit in the future and may have to pay higher interests on existing debts as well as any new obligations. From an investor's point of view, he would want to invest in a company if it is capable of handling its financial obligations, can grow quickly, and is able to manage the growth scale.
A balance sheet is a financial statement of a company that provides a snapshot of what a company owns, owes, and the amount invested by the shareholders. Thus, it is an important tool that helps evaluate the performance of a business. We study balance sheets of 3586 indian companies with their 66 predictor variables to predict if a company will default next year.
We use Logistic Regression on multiple models treated differently.
The final model chosen has the Accuracy = 96% and Recall = 95%.
Logistic Regression, Exploratory Data Analysis, Machine Learning, Classification