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Extensive Analysis + Preprocessing + Modelling On Loan Repayment System

Many people struggle to get loans due to insufficient or non-existent credit histories. And, unfortunately, this population is often taken advantage of by untrustworthy lenders.

Home Credit makes use of a variety of alternative data--including telco and transactional information--to predict their clients' repayment abilities.

While Home Credit is currently using various statistical and machine learning methods to make these predictions, they're challenging Kagglers to help them unlock the full potential of their data. Doing so will ensure that clients capable of repayment are not rejected and that loans are given with a principal, maturity, and repayment calendar that will empower their clients to be successful.

I got this dataset from Kagglers to also help them unlock the full potential of there dataset

1. The problem statement

In this kernel, we will try to answer the question that whether or not the loan should be repaid. We implement Logistic Regression with Python and Scikit-Learn.

To answer the question, we build a classifier to predict whether or not the loan should be repaid. We train a binary classification model using Logistic Regression.

So, let's get started.