# Home-Credit-Default-Risks

**Repository Path**: as11221208/Home-Credit-Default-Risks

## Basic Information

- **Project Name**: Home-Credit-Default-Risks
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2018-09-08
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Home-Credit-Default-Risks
Predicting how capable a loan applicant will be at repaying the loan or defaulting. 
![alice-pasqual-258250-unsplash](https://user-images.githubusercontent.com/35437820/40391593-d4978684-5de6-11e8-96af-4b6231cec8a9.jpg)
<a style="background-color:black;color:white;text-decoration:none;padding:4px 6px;font-family:-apple-system, BlinkMacSystemFont, &quot;San Francisco&quot;, &quot;Helvetica Neue&quot;, Helvetica, Ubuntu, Roboto, Noto, &quot;Segoe UI&quot;, Arial, sans-serif;font-size:12px;font-weight:bold;line-height:1.2;display:inline-block;border-radius:3px;" href="https://unsplash.com/@stri_khedonia?utm_medium=referral&amp;utm_campaign=photographer-credit&amp;utm_content=creditBadge" target="_blank" rel="noopener noreferrer" title="Download free do whatever you want high-resolution photos from Alice Pasqual"><span style="display:inline-block;padding:2px 3px;"><svg xmlns="http://www.w3.org/2000/svg" style="height:12px;width:auto;position:relative;vertical-align:middle;top:-1px;fill:white;" viewBox="0 0 32 32"><title>unsplash-logo</title><path d="M20.8 18.1c0 2.7-2.2 4.8-4.8 4.8s-4.8-2.1-4.8-4.8c0-2.7 2.2-4.8 4.8-4.8 2.7.1 4.8 2.2 4.8 4.8zm11.2-7.4v14.9c0 2.3-1.9 4.3-4.3 4.3h-23.4c-2.4 0-4.3-1.9-4.3-4.3v-15c0-2.3 1.9-4.3 4.3-4.3h3.7l.8-2.3c.4-1.1 1.7-2 2.9-2h8.6c1.2 0 2.5.9 2.9 2l.8 2.4h3.7c2.4 0 4.3 1.9 4.3 4.3zm-8.6 7.5c0-4.1-3.3-7.5-7.5-7.5-4.1 0-7.5 3.4-7.5 7.5s3.3 7.5 7.5 7.5c4.2-.1 7.5-3.4 7.5-7.5z"></path></svg></span><span style="display:inline-block;padding:2px 3px;">Alice Pasqual</span></a>  *Photo Credit

# Background
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.

# Data
This project is cureently an active Kaggle competition I will be competing in. Data was collected at the link listed below:
https://www.kaggle.com/c/home-credit-default-risk/data

** More to come as I work on the competition.