Credit_Risk_Analysis

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Overview of Credit Card Analysis

In this project, RandomOverSampler and SMOTE algorithms were used to perform oversampling, ClusterCentroids algorithm was used to undersampling, SMOTEENN algorithm was applied as a combinatorial approach of over- and undersampling of credit card credit dataset from LendingClub. Machine learning models - BalancedRandomForestClassifier and EasyEnsembleClassifier were used to predict credit risk.

Results

1. Naive Random Oversampling

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2. SMOTE Oversampling

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3. Undersampling

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4. Combination (Over and Under) Sampling

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5. Balanced Random Forest Classifier

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6. Easy Ensemble AdaBoost Classifier

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Summary

1. Comparing Credit Risk Resampling to Ensemble Techniques, it is clear that higher credit risk prediction accuracy was observed with Easy Ensemble AdaBoost Classifier of 93%. It is recommended that Easy Ensemble AdaBoost Classifier be used to reduce bias in prediction.