Logistic Regression using Blue sky statistics

Case Study Context: The video discusses a logistic regression case study involving micro mortgage analytics, focusing on the challenges faced by individuals in India's unorganized sector when applying for home loans due to lack of proper documentation.
Problem Statement: Gjan Housing Finance Limited aims to address these challenges but needs to thoroughly verify applicants' creditworthiness, which is costly. The goal is to build a model to predict loan rejection at an early stage to save verification costs.
Logistic Regression Explanation: Logistic regression is introduced as a method to identify the probability of loan approval (Y=1) or rejection (Y=0) based on independent variables, particularly the Loan-to-Value (LTV) ratio, which compares the loan amount to the market value of the property.
Model Implementation: Using Blue Sky Statistics, the video demonstrates how to perform logistic regression with LTV as the independent variable and decision (loan approval or rejection) as the dependent variable, explaining how to interpret the model's output.
Probability Calculation: The video provides a step-by-step guide on calculating the probability of loan approval using the logistic regression model, illustrating how different LTV ratios affect approval chances and demonstrating the process with Excel for practical understanding.