GitHub topics: resampled-data
blleshi/Credit_Risk_Classification
Credit Risk Classification
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YujiSODE/testingFtU
Statistical test(F-test, t-test, and U-test) with two numerical arrays.
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Asheladia/classification_risky_business
In this assignment, I have built and evaluate several machine-learning models to predict credit risk using free data from LendingClub. Credit risk is an inherently imbalanced classification problem (the number of good loans is much larger than the number of at-risk loans), so I needed to employ different techniques for training and evaluating models with imbalanced classes. You will see use of the imbalanced-learn and Scikit-learn libraries to build and evaluate models using the two following techniques: Resampling and Ensemble Learning.
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