robo-credit-underwriter-multi-rl

Credit Risk analysis by using Python and ML

scikit-learn compatible tools for building credit risk acceptance models

Modeled the credit risk associated with consumer loans. Performed exploratory data analysis (EDA), preprocessing of continuous and discrete variables using various techniques depending on the feature. Checked for missing values and cleaned the data. Built the probability of default model using Logistic Regression. Visualized all the results. Computed Weight of Evidence and price elasticities.

A python framework for risk scoring

Machine learning for financial risk management

Credit Risk Analysis with Machine Learning

Credit Card Fraud Detection Project

A mirror of the Open Risk white paper collection

A comprehensive machine learning solution for assessing credit risk in the banking sector. This project leverages advanced algorithms to evaluate and classify the creditworthiness of customers, aiming to enhance decision-making in loan approvals and risk management.

This is our second project at neuefische DS Bootcamp. Silas Mederer (https://github.com/sls-mdr) and me applied different ML models and for credit default prediction of the P2P platform Lending Club.