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

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

Creating various machine learning models to create the most accurate model to predict credit risk

Supervised Machine Learning and Credit Risk

Supervised machine learning model to analyze credit risk

Supervised Machine Learning Project

using machine learning to assess credit risk

About Six different techniques are employed to train and evaluate models with unbalanced classes. Algorithms are used to predict credit risk. Performance of these different models is compared and recommendations are suggested based on results. Topics

Uses several machine learning models to predict 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

Built, trained and evaluated multiple supervised machine learning algorithms to predict credit risk for loan applicants. Algorithms ran include Random Oversampler, SMOTE, Cluster Centroids, SMOTEENN, Balanced Random Forest Classifier, and Easy Ensemble Classifier.

Supervised Machine Learning and Credit Risk

Compared the effectiveness of the EasyEnsembleClassifier and LogisticRegression libraries. This was to assess the model with the best scores for balanced accuracy, recall, and geometric mean.

Credit Risk Analysis utilizing imbalanced classification machine learning models

Supervised Machine Learning and Credit Risk

For this analysis, we used computational linguistics and biometrics to systematically identify the trend using various news articles and closing prices using the "CoinGecko CSV & Crypto News API"!

Using machine learning to train and evaluate models with unbalanced classes to determine the best models to predict credit risk.

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.

Six different techniques are employed to train and evaluate models with unbalanced classes. Algorithms are used to predict credit risk. Performance of these different models is compared and recommendations are suggested based on results.