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.

Overview

header.png

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

Random.png

2. SMOTE Oversampling

smote.png

3. Undersampling

undersampling.pngg

4. Combination (Over and Under) Sampling

combination.PNG

5. Balanced Random Forest Classifier

balanced.png

6. Easy Ensemble AdaBoost Classifier

easy.png

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.

Owner
Chukwuyem Charles Obuseh
Self-driven individual, analytical, problem solver with over 10 years of experience in the oil and gas industry and 3 years leveraging data engineering skills.
Chukwuyem Charles Obuseh
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