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Assess credit risk of applicants using supervised machine learning. Several different machine learning techniques such as SMOTE, SMOTEENN, RANDOM FOREST, EASY ENSEMBLE were applied, the models were assessed using accuracy score, precision and accuracy to choose the best technique that applies to this type of problem.

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Credit_Risk_Analysis

Overview of the Analysis

This analysis is done to assess credit risk of applicants using machine learning. Several different machine learning techniques are applied , then the models are assessed to choose the best technique that applies to this type of problem.

Results:

1.Random oversampler

-The balanced accuracy score for the random oversampler is 0.65, which tells that the model has predict 65% of the test sample correctly.

-The model has very low precision (close to 0) in detecting a high risk, and a very high precision in detecting low risk applicants(100%).

-The model has moderate sensitivity (0.63) in detecting a high risk, and a moderate sensitivity (0.67) in detecting low risk applicants.

2.SMOTE oversampling

-The balanced accuracy score for the random oversampler is 0.64, which tells that the model has predict 64% of the test sample correctly.

-The model has very low precision (close to 0) in detecting a high risk, and a very high precision in detecting low risk applicants (100%).

-The model has moderate sensitivity (0.61) in detecting a high risk, and a moderate sensitivity (0.67) in detecting low risk applicants.

3.Undersampling

-The balanced accuracy score for the random oversampler is 0.51, which tells that the model has predict 51% of the test sample correctly.

-The model has very low precision (close to 0) in detecting a high risk, and a very high precision in detecting low risk applicants(100%).

-The model has moderate sensitivity (0.59) in detecting a high risk, and a moderate sensitivity (0.44) in detecting low risk applicants.

4.SMOTEENN

-The balanced accuracy score for the random oversampler is 0.64, which tells that the model has predict 64% of the test sample correctly.

-The model has very low precision (close to 0) in detecting a high risk, and a very high precision in detecting low risk applicants(100%).

-The model has moderate sensitivity (0.70) in detecting a high risk, and a moderate sensitivity (0.58) in detecting low risk applicants.

5.Balanced Random Forest Classifier

-The balanced accuracy score for the random oversampler is 0.79, which tells that the model has predict 79% of the test sample correctly.

-The model has very low precision (close to 0) in detecting a high risk, and a very high precision in detecting low risk applicants(100%).

-The model has moderate sensitivity (0.70) in detecting a high risk, and a high sensitivity (0.89) in detecting low risk applicants.

6.Easy Ensemble Classifier

-The balanced accuracy score for the random oversampler is 0.92, which tells that the model has predict 92% of the test sample correctly.

-The model has very low precision (close to 0) in detecting a high risk, and a very high precision in detecting low risk applicants(100%).

-The model has high sensitivity (0.91) in detecting a high risk, and a high sensitivity (0.94) in detecting low risk applicants.

Summary:

Since the business is to check the risk of the applicant, the most important aspect is if the model misses a high risk applicant and lables the applicant as low risk.

So, all the models reslts show moderate sensitivity in detecting high risk except for the EasyEnsembleClassifier model which has a very high sensitivity of 0.94 in detecting high risk applicants. Also, this models has a 92% balanced accuracy score which is a very good score in prediciting the risk.

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Assess credit risk of applicants using supervised machine learning. Several different machine learning techniques such as SMOTE, SMOTEENN, RANDOM FOREST, EASY ENSEMBLE were applied, the models were assessed using accuracy score, precision and accuracy to choose the best technique that applies to this type of problem.

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