Open solution to the Home Credit Default Risk challenge 🏡

ESC Team's scorecard tools

Credit Risk analysis by using Python and ML

Weight of Evidence，基于iv值最大思想求最优分箱

Credit scoring modeling toolbox based on R

Ranked 10/7198 (Top 1%) One of the models used in Kaggle Home Credit Default Risk.

A predictive model that uses several machine learning algorithms to predict the eligibility of loan applicants based on several factors

Demonstrating technical elements in support of open source securitisation frameworks

A short course on survival analysis applied to the financial industry

A python package for credit risk card.

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.

This project commissions to examine the 100,000 credit card application data, detect abnormality and potential fraud in the dataset. All data manipulation and analysis are conducted in R. Featured analysis methods include Principal Component Analysis (PCA), Heuristic Algorithm and Autoencoder.

MSc Dissertation on Credit Risk Modeling

Objective of this competition is to use historical loan application data to predict whether or not an applicant will be able to repay a loan.

Using various machine learning models to predict whether a company will go bankrupt

Financial risks of bonds

A mirror of the open risk white paper collection

Course draft - bank corporate lending

Creating an India credit risk(default) Logistic Regression model

By the data set from 'Give Me Some Credit' (2012), this work is to use it to illustrate some useful techniques in Credit Scoring Modelling, namely: GLM, SMOTE, CARET, CHAID, and MOB.

Home Credit Default Risk in Python

Efficient simulation in the CreditMetrics model with Julia

Links para as configurações de ambiente dos minicurso realizados no 2 Meetup do PyData Manaus

Model calculation with macroeconomic influence - estimate of debtor's probability of going default