In the financial sector, assessing an applicant's credit risk and determining the appropriate loan amount are critical for both the financial institution and the customer. Misjudging an applicant's risk can lead to financial losses for the institution. Similarly, inaccurately estimating the loan amount can either burden the customer with more than they can handle or limit their potential to use the loan effectively. Therefore, developing predictive models that can accurately classify credit risk and estimate suitable loan amounts based on various financial indicators is essential. Objectives 1. Credit Risk Classification - To develop a machine learning model that classifies applicants into categories of credit risk 2. Loan Amount Prediction - To create a regression model that accurately predicts the loan amount that should be approved for applicants based on their financial health indicators. This document evaluates the Random Forest, Logistic regression, LightGBM and Xgboost.