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
Supervised Machine Learning and Credit Risk
Testing 6 different machine learning models to determine which is best at predicting credit risk.
Resampling exercise to predict accuracy, precision, and sensitivity in credit-loan risk
Testing various supervised machine learning models to predict a loan applicant's credit risk.
Supervised Machine Learning Project
using machine learning to assess credit risk
Analyze of several Machine Learning techniques in order to help Jill decide on a most effective Machine Learning Model to analyze Credit Card Risk applications.
Build and evaluate several machine learning algorithms to predict 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.
Banking-Dataset-Marketing-Targets
Using Supervised Machine Learning algorithms to identify credit risks
Supervised Machine Learning
Supervised Machine Learning and Credit Risk
Build and evaluate several machine learning algorithms to predict credit risk
Use scikit-learn and imbalanced-learn machine learning libraries to assess credit card risk.
Build and evaluate several machine learning algorithms by resampling models to predict credit risk.
Determine supervised machine learning model that can accurately predict credit risk using python's sklearn library. Python, Pandas, imbalanced-learn, skikit-learn
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