In this report, I explore a bank dataset containing information about individuals who have applied for credit. Each entry in the dataset represents a loan applicant, classified as either a good credit risk or a bad credit risk based on their attributes. The primary goal of this analysis is to develop a predictive model that can assist the bank in determining whether to approve a loan for a prospective applicant.

This analysis specifically focuses on women, as they are particularly susceptible to certain diabetes risk factors, including pregnancy-related factors. Pregnancies, especially those resulting in gestational diabetes, can increase the likelihood of developing type 2 diabetes later in life.