Credit Scorecard Model Using Logistic Regression (Study Case: Home Credit)

Introduction

This notebook was made as a Project-Based Internships at Home Credit

Problem Statement

An important fraction of the population finds it difficult to get their loans approved due to insufficient or absent credit history. Conversely, it is a major challenge for banks and other finance lending agencies to decide for which candidates to approve loans. In order to make sure this underserved population has a positive loan experience, Home Credit makes use of a variety of alternative data to predict their clients' repayment abilities.

Goals and Objective

Create a credit scoring system where the inputs are various features describing the financial and behavioral history of the loan applicants, in order to automatically predict whether the loan will be repaid or defaulted.

Dataset Information

This dataset has 307,511 rows dan 122 columns

Results: