hcdr
OpenML dataset with id 45071
Author name not available (Why is that?)
Full work available at URL: https://api.openml.org/data/v1/download/22112029/hcdr.arff
Upload date: 27 January 2023
Dataset Characteristics
Number of classes: 0
Number of features: 70 (numeric: 23, symbolic: 47 and in total binary: 33 )
Number of instances: 244,280
Number of instances with missing values: 0
Number of missing values: 0
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 strives to broaden financial inclusion for the unbanked population by providing a positive and safe borrowing experience. In order to make sure this underserved population has a positive loan experience, 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.
This page was built for dataset: hcdr
