Machine Learning analysis on R to predict customer defaults

Amex Default Prediction

Working with an industrial scale data set to build a classification model to predict credit card default, and help creating a better customer experience for cardholders.

Finance and Risk Analytics Project: Predicting credit default risk using machine learning models (Logistic Regression, Random Forest) and assessing stock market risk through historical returns and volatility analysis to guide financial risk management and investment strategies.

The goal of this project is to perform default prediction for commercial real estate property loans based on 17 variables.

In this project, task is to help banking organization to identify the right customers using predictive models. Using past data of the bank’s applicants, you need to determine the factors affecting credit risk, create strategies to mitigate the acquisition risk and assess the financial benefit of the project.

NYU DS-GA 3001 Final Project

A group assignment on Machine Learning.

Python

Machine learning model to identify customers that are more likely to default based on employment, bank balance and annual salary.

A program to take in loan level data and create a model which can predict probability of default

Classification model to predict the probability that a customer defaults based on their monthly customer statements using the data provided by American Express.