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LOAN DEFAULT PREDICTION USING IMPERIAL COLLEGE LONDON DATASET

This project aims to use machine learning models to determine the likelihood of loan default, in today’s corporate world, lending and borrowing money from financial institutions creates new chances for financial institutions; most banks and credit unions rely heavily on loan interest and associated fees for revenue, yet, there is a risk of suffering losses due to loan defaulters. Lending money to individuals has become a common business activity for financial organisations. Every day, people seek money from various financial institutions for a variety of reasons. In contrast, not every individual in need of a loan is trustworthy, and not all persons are eligible for loans. Furthermore, a significant proportion of people fail to repay the amount provided by lending institutions on a yearly basis, causing these organisations to incur significant losses

#DATASET LINK: Imperial College London dataset https://www.kaggle.com/c/loan-default-prediction/data

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The project aims to develop a predictive model for loan default using the dataset given by Imperial College London. The goal is to analyse the data, understand the factors that contribute to loan defaults, and create a machine learning model that can forecast the likelihood of loan default for future borrowers.

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