Credit-Risk-Analysis

Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook

iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data

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

Implementation of feature engineering from Feature engineering strategies for credit card fraud

Machine learning for financial risk management

Credit Card Fraud Detection Project

Using simple logistic regression to predict credit card fraud transactions