Predicting Loan Default using Logistic Regression

Analysing borrower data to determine the likelihood that a loan would default is the process of loan default prediction using logistic regression. Because it determines the probability of a binary outcome (default or no default) based on input features, logistic regression is a common option for this binary classification assignment. Historical loan data, including credit score, income, loan amount, work status, and past repayment history, is gathered in order to construct the model. After that, the data undergoes preprocessing, whereby missing values are addressed and categorical variables are encoded as necessary. By fitting a sigmoid function to the data, logistic regression then determines the correlation between these characteristics and the chance of default, producing probabilities ranging from 0 to 1 for every loan application.

With a probability threshold set to decide whether a loan is deemed high risk, the model can be used to forecast default risk for new loan applications after training. Financial institutions frequently utilise this method for risk assessment and decision-making because it is easy to understand, statistically sound, and useful for identifying the variables that affect default risk.