You can find the Analysis file here: credit_risk_resampling.ipynb | credit_risk_ensemble.ipynb
We create and analyse multiple machine learning models to forecast credit risk using Python in this project. The following technique was used:
- Using the RandomOverSampler and SMOTE algorithms, oversample the data.
- Using the ClusterCentroids technique, undersample the data.
- Using the SMOTEENN method, take a combinatorial approach to over- and undersampling.
- BalancedRandomForestClassifier and EasyEnsembleClassifier are two machine learning models that eliminate bias.
We'll assess these models' performance and offer a recommendation on whether or not they should be utilised to predict credit risk.
All of the credit risk analysis models have low accuracy in assessing if a credit risk is high. The Ensemble models resulted in significant improvements, particularly in the sensitivity of high-risk loans. With a recall of 92 percent, the EasyEnsembleClassifier model can detect virtually all high-risk credit. On the other hand, because of the poor accuracy, many low-risk credits are still misclassified as high-risk, putting the bank's credit strategy at risk and causing it to miss out on income prospects. As a result, I would advise the bank against using any of these algorithms to anticipate credit risk.
- Email : [email protected]
- Linkedin: www.linkedin.com/in/neda-ahmadi-j