Credit risk is an inherently unbalanced classification problem, as good loans easily outnumber risky loans. Therefore, there is a need to employ different techniques to train and evaluate models with unbalanced classes.

• To apply resampling models, SMOTEENN algorithm and Ensemble classifiers to predict credit risk.

Dataset: LoanStat_2019Q1.csv, Python 3.7.6 and Anaconda 2020.11

The balanced accuracy score was 65% and the precision for the high risk had a very low positivity at 1% with a recall of 69% and the F1 was 2%. The higher number of populations for low risk resulted a 100% precision with a recall of 61%.

Based on this model the balanced accuracy score was 66%, precision (1%), and F1 (2%) for high risk results were smilar to oversampling model. Due to the higher population for lowrisk, the precision for low risk was 100% with a recall of 69%.

The accuracy score for undersampling technique was 56%. The precision, recall and F1 for high risk were 1%, 65% and 1%, respectively. However, the precision and recall for low risk were 100% and 47%.

Based on this model the accuracy score was 64% and the high risk precision, recall and F1 were 1%, 72% and 2%, respectively. And the precision and recall for low risk were 100% and 57%.

The Random Forest model shows the accuracy score of 68% and the precision, recall and F1 for high risk were 88%, 37% and 52%, respectively. The precision and recall for low risk were 100%.

The balanced accuracy score was 93% and the precision for high risk was 9% with 92% recall. The precision and recall for low risk were 100% and 94%, respectively.

Below is the summary of the top three models which scored the highest balanced accuracy scores:-

• Easy Ensemble Classifier 93%.
• Random Forest 68%.
• SMOTE 66%.

Regarding Random Forest model, the precision and recall for low risk were 100%, however the accuracy score was lower than Ensemble Classifier. The Easy Ensemble model has predicted 93 observations as actual high risk (true positive) out of 101 observations (92%). Similarly, the actual low risk was predicted by 94% as true negative (16121 out of 17104). Additionally, the accuracy score for Ensemble model was 93%. Therefore, I would recommend the Easy Ensemble Classifier to use for prediction credit risk.