Predicts credit risk of individuals based on information within their application utilizing supervised machine learning models
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Updated
Aug 27, 2024 - Jupyter Notebook
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.
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