Loan Default Prediction Dataset

Banks run into losses when customers don't pay their loans on time. Because of this, every year, banks have losses in crores, and this also impacts the country's economic growth to a large extent. In this hackathon, we look at various attributes such as funded amount, location, loan, balance, etc., to predict whether a person will be a loan defaulter.

To solve this problem, MachineHack has created a training dataset of 67,463 rows and 35 columns and a testing dataset of 28,913 rows and 34 columns. The hackathon demands a few pre-requisite skills like big datasets, underfitting vs overfitting, and the ability to optimize “log_loss” to generalize well on unseen data.