Credit_Risk_Analysis

Overview of the analysis:

This exercise is to employ different techniques to train and evaluate different machine learning models to predict credit risk with unbalanced classes. Algorithms used in the analysis:

• the oversampling RandomOverSampler and SMOTE algorithms.
• the undersampling ClusterCentroids algorithm to resample the data.
• the combinatorial SMOTEENN algorithm to resample the training data.
• BalancedRandomForestClassifier and EasyEnsembleClassifier to reduce bias.

Results:

We use balanced accuracy score, confusion matrix and imbalanced classification report to compare results.

RandomOverSampler

< <img src='https://user-images.githubusercontent.com/90880564/150888883-bf5fbdea-e210-446a-92cc-3b4dd66dd5c3.png?raw=true" alt="Sublime's custom image"/>

The balanced accuracy score is 62%.
The high_risk precision is about 1% only with 60% sensitivity which makes a F1 of 2% only.
Due to the imbalanced number of the low_risk population, its precision is almost 100% with a sensitivity of 65%.

SMOTE

<img src='https://user-images.githubusercontent.com/90880564/150889306-0cd2943c-4c6a-44bb-8533-9544e9e2a87d.png?raw=true" alt="Sublime's custom image"/> <img src='https://user-images.githubusercontent.com/90880564/150889407-bc55ec13-a0d5-4ac6-acbe-caac05dbf50e.png?raw=true" alt="Sublime's custom image"/>

The balanced accuracy score is 65%.
The high_risk precision is about 1% only with 64% sensitivity which makes a F1 of 2% only.
Due to the imbalanced number of the low_risk population, its precision is almost 100% with a sensitivity of 66%.
Very similiar result to the previous one.

ClusterCentroids

<img src='https://user-images.githubusercontent.com/90880564/150902602-c05a27b8-8aee-4e4b-86db-3b13940f9d81.png?raw=true" alt="Sublime's custom image"/>

The balanced accuracy score is down to 52%.
The high_risk precision is about 1% only with 59% sensitivity which makes a F1 of 1% only.
Due to the imbalanced number of the low_risk population, its precision is almost 100% with a sensitivity of 46%.

Combinatorial SMOTEENN

<img src='https://user-images.githubusercontent.com/90880564/150903138-4d353ed2-fcbc-4512-b38e-f3fe8c413168.png?raw=true" alt="Sublime's custom image"/>

The balanced accuracy score is 62%.
The high_risk precision is about 1% only with 69% sensitivity which makes a F1 of 2%.
Due to the imbalanced number of the low_risk population, its precision is almost 100% with a sensitivity of 54%.

BalancedRandomForestClassifier

<img src='https://user-images.githubusercontent.com/90880564/150904192-4db6b157-2123-4544-a10b-6695abe7943b.png?raw=true" alt="Sublime's custom image"/>

The balanced accuracy score is greatly improved to 79%.
The high_risk precision is about 4% only with 67% sensitivity which makes a F1 of 7%.
Due to a lower number of false positives, its precision is almost 100% with a sensitivity of 91%.

EasyEnsembleClassifier

<img src='https://user-images.githubusercontent.com/90880564/150904902-5b072f7f-ff0a-483a-a6ab-983c4751a8a3.png?raw=true" alt="Sublime's custom image"/>

The balanced accuracy score is very high at 93%.
The high_risk precision is about 7% only with 91% sensitivity which makes a F1 of 14%.
Due to a lower number of false positives, its precision is almost 100% with a sensitivity of 94%.

Summary:

• All the models we used to predict the credit risk analysis show weak precision in determining if a credit risk is high.
• The Ensemble models show great improvment specially on the sensitivity of the high risk credits.
• Even though the EasyEnsembleClassifier model detects almost all high risk credit. On another hand, with a low precision, a lot of low risk credits are still falsely detected as high risk. It may lead to the bank losing its business opportunities.
• Maybe there are models the bank can use to predict credit risk other than those above.