Credit_Risk_Analysis_Report

Overview of the analysis:

Purpose

For this analysis I will need to employ different techniques to train and evaluate models with unbalanced classes. Mainly I will use imbalanced-learn and scikit-learn libraries to build and evaluate models using resampling. Using the credit card credit dataset from LendingClub, a peer-to-peer lending services company, I’ll oversample the data using the RandomOverSampler and SMOTE algorithms, and undersample the data using the ClusterCentroids algorithm. Then, I’ll use a combinatorial approach of over- and undersampling using the SMOTEENN algorithm. Next, I’ll compare two new machine learning models that reduce bias, BalancedRandomForestClassifier and EasyEnsembleClassifier, to predict credit risk. Once you’re done, I’ll evaluate the performance of these models and make a written recommendation on whether they should be used to predict credit risk.

The results will include the following:

• Deliverable 1: Use Resampling Models to Predict Credit Risk.
• Deliverable 2: Use the SMOTEENN Algorithm to Predict Credit Risk.
• Deliverable 3: Use Ensemble Classifiers to Predict Credit Risk.
• Deliverable 4: A Written Report on the Credit Risk Analysis (README.md).

Results:

The results for the six machine learning models (Naive Random Oversampling, SMOTE Oversampling, Undersampling, Combination (Over and Under) Sampling, Balanced Random Forest Classifier, and Easy Ensemble AdaBoost Classifier) shows the output for their respective balanced accuracy, precision, and recall scores.

The results are as follows:

• Resampling Models to Predict Credit Risk:
  • Naive Random Oversampling

The balanced accuracy score is 0.6348870972223508. The precision for high and low risk are 0.01 and 1.00 respectively. The recall scores for high and low risk are: 0.66 and 0.61 respectively.

• SMOTEENN Algorithm to Predict Credit Risk
  • SMOTE Oversampling

The balanced accuracy score is 0.6591200367698136. The precision for high and low risk are 0.01 and 1.00 respectively. The recall scores for high and low risk are: 0.63 and 0.68 respectively.

• Undersampling

The balanced accuracy score is 0.6591200367698136. The precision for high and low risk are 0.01 and 1.00 respectively. The recall scores for high and low risk are: 0.69 and 0.40 respectively.

• Combination (Over and Under) Sampling

The balanced accuracy score is 0.5443246441108096. The precision for high and low risk are 0.01 and 1.00 respectively. The recall scores for high and low risk are: 0.72 and 0.57 respectively.

• Use Ensemble Classifiers to Predict Credit Risk
  • Balanced Random Forest Classifier

The balanced accuracy score is 0.78776726253066. The precision for high and low risk are 0.04 and 1.00 respectively. The recall scores for high and low risk are: 0.67 and 0.91 respectively.

• AdaEasy Ensemble AdaBoost Classifier

The balanced accuracy score is 0.925427358175101. The precision for high and low risk are 0.07 and 1.00 respectively. The recall scores for high and low risk are: 0.91 and 0.94 respectively.

Summary:

When looking at the results of each machine learning models, we want the model(s) that are closer to 1 and not 0. We can see that the Easy Ensemble AdaBoost Classifier model has a higher balanced accuracy, 0.93, compared to other 5 models. This means that is the closest to 1. The other 5 models are below the 0.79 balanced accuracy.

Regarding the precision results for the machine learning models the first 4 models the high and low risks were all similar 0.01 and 1.00 respectively, whereas the last two their precision results were different for the high risk 0.04 and 0.07 and low risk were the same but still within the 1 range, which is good.

The recall scores for all the machine learning models are good because they all fall under the 1 range. From all of these results, the model that is closer to 1 in all results is the AdaEasy Ensemble AdaBoost Classifier.