Credit scoring machine learning algorithm which predicts probability of default

A decentralised credit scoring platform based on Multichain blockchain

Credit Score Provider for the Faker Python Project. Use this to generate fake but realistic-looking consumer credit scores aligning to the most prevalent risk models (FICO, VantageScore, etc.)

Implement supervised machine learning techniques in order to further understanding the process in which a client will be granted a credit and be denied a credit.

Feature Selection for Credit Scoring using Genetic Algorithm Wrapper(Information Gain)

A streamlit app on credit score classification

Reverse engineering of the FICO algorithm

The official ARCx contracts repo

An Exploratory Data Analysis was done on the Credit Card dataset using libraries in R . The EDA insights were used to create a report.

Credit card score classification using bank dataset with machine learning

This repo contains analysis of Lending Club Credit rates and also case study for a client to get a fully funded loan at the lowest credit rate with a desired duration.

canvas实现类似支付宝信用分的效果(演示的是非模块版本的，codesandbox的index2.html是模块传参的）

Classification Model for Credit Score using Logistic Regression, Evaluation using AUC and KS

How to create a credit score model in Layer

In this project, I used a dataset containing financial information and personal details of individuals to build and evaluate machine learning models to predict their credit scores. The credit score is an important metric that determines an individual's creditworthiness and ability to obtain loans.

Projeto de criação de modelo de machine learning para score de credito, percorrendo todo o pipeline dos dados. Coleta, exploração, tratamento, limpeza, treino e deploy.

The creation of the credit score models of a lending company.

A Flutter package

Research Report on TrueAccord, a FinTech company founded in 2013 and based out of San Francisco, that is revolutionizing the debt collection industry.

By performing feature engineering, visualization, and data cleaning, analysts can gain valuable insights into credit score data. These steps help in understanding the factors influencing credit scores, and identifying potential data quality issues,