Supervised Machine Learning and Credit Risk

Credit Risk Analysis utilizing imbalanced classification machine learning models

Uses several machine learning models to predict credit risk.

Machine learning models for predicting credit risk in LendingClub dataset.

Supervised scikit-learn machine learning models using several sampling techniques.

A Deep Learning analysis to predict success of charity campaigns

Data preparation, statistical reasoning and machine learning are used to solve an unbalanced classification problem. Different techniques are employed to train and evaluate models with unbalanced classes.

The purpose of this study is to recommend whether PureLending should use machine learning to predict credit risk. Several machine learning models are built employing different techniques, then they are compared and analyzed to provide the recommendation.

Supervised Machine Learning

Extract data provided by lending club, and transform it to be useable by predictive models.

Credit Risk Analysis utilizing imbalanced classification machine learning models

Predicts credit risk of individuals based on information within their application utilizing supervised machine learning models