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Credit_Risk_Analysis 🚩 💳 🚩

🔳 Overview of the analysis:

Using machine learning, this analyzes credit card risk using six machine learning models. We have employed different techniques to train and evaluate models with unbalanced classes. We are using imbalance-learn and scikit-learn libraries to build and evaluate models using resampling.

🔳 Results:

A low precision is indicative of a large number of false positives.

A low recall is indicative of a large number of false negatives.

Model Balanced Accuracy Score Precision Recall F1
Oversampling 64% 0.99 0.60 0.74
SMOTE 66% 0.99 0.69 0.81
ClusterCentroid 66% 0.99 0.40 0.56
SMOTEENN 54% 0.99 0.57 0.72
Balanced Random Forest 68% 1.00 1.00 1.00
AdaBoost 69% 1.00 1.00 1.00

🔳 Summary:

The models with the highest F1 score were Balanced Random Classifier and AdaBoost both with an F1 score of 1.00. However, SMOTE had an F1 score of 0.81 and a low recall of 0.69. Indicating that SMOTE is a more accurate model.

https://github.com/AJMnd/Credit_Risk_Analysis/blob/main/Resources/SMOTE.png

https://github.com/AJMnd/Credit_Risk_Analysis/blob/main/Resources/Rfc.png

https://github.com/AJMnd/Credit_Risk_Analysis/blob/main/Resources/Ada.png