Problem Statement

Credit Risk Modeling for Loan Approvals A regional bank wants to improve its loan approval process by developing a machine learning model that can predict the likelihood of loan default. The bank has been collecting data on loan applic ants over the past few years, including their personal information, fin ancial histor y, and loan details. Your task is to build a machine learning model that will help the bank make informed decisions about lo an application s and reduce their credit risk. The bank's risk management team wants to understand which factors contribute most to loan defaults so they can adjust their lending criteria and pricing strategies. They also want to automate the initial screening process to handle the increasing volume of loan applications more efficiently. Task: Your task is to pre process the dataset and build a machine learni ng model to predict whether a loan applicant is likely to de fault on their loan (binary classification problem). Dataset Description train.