Built and evaluated several machine learning algorithms to predict credit risk.

Built several supervised machine learning models to predict the credit risk of candidates seeking loans.

The Repository is created to cover undersampling and oversampling methods to deal imbalance problem.

Machine-learning models to predict credit risk using free data from LendingClub. Imbalanced-learn and Scikit-learn libraries to build and evaluate models by using Resampling and Ensemble Learning

The objective of this analysis was to use machine learning models to accurately predict credit risk.

Using Resampling and Ensemble Learning to look at data and predict default rates on loans.

Perform a Credit Risk Supervised Machin Learning Analysis using scikit-learn and imbalanced-learn libraries.

Analysis of different machine learning models' performance on predicting credit default

Build and evaluate several machine learning algorithms to predict credit risk

Supervised Learning..Build/Evaluate Machine algorithms to predict credit risk

Utilizing data preparation, statistical reasoning, and supervised machine learning to solve a real-world challenge: credit card risk.

The purpose of this analysis was to create a supervised machine learning model that could accurately predict credit risk using machine learning.

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

Using machine learning to determine which model is best at predicting credit risk amongst random oversampling, SMOTE, ClusterCentroids, SMOTEENN, Balanced Random Forest, or Easy Ensemble Classifier (AdaBoost).

An analysis on credit risk

Testing various supervised machine learning models to predict a loan applicant's credit risk.

Build and evaluate several machine learning algorithms by resampling models to predict credit risk.

Analysis of a dataset using different techniques to train and evaluate models with unbalanced classes, aimed at reducing bias and predicting accurate credit risk.

Established a supervised machine learning model trained and tested on credit risk data through a variety of methods to establish credit risk based on a number of factor

Supervised Machine Learning project to predict credit risk