Credit Risk Analysis

Overview of Analysis

In this analysis, I use Machine Learning to predict the credit risk of candidates seeking loans. I test several Machine Learning models to maintain a model that predicts with high accuracy according to the classification report. After preprocessing the data, using the train_test_split function of Python I split the dataset into a train and test dataset. I then use four resampling techniques and two ensemble algorithms to test the model.

Results

Resampling Models to Predict Credit Risk

Since I am trying to identify good candidates for loans I am going to assume that as a bank we prefer finding a good candidate over one that would default and don't mind losing customers for this reason. So throughout my analysis, I will consider the recall score as more important.

Oversampling

1. RandomOverSampler

• Accuracy Score: 65%
• Precision Score: 99%
• Recall Score: 68%

Table 1: Analysis using RandomOverSampler

2. SMOTE Oversampling

• Accuracy Score: 62%
• Precision Score: 99%
• Recall Score: 64%

Table 2: Analysis using SMOTE

Undersampling

1. ClusterCentroids

• Accuracy Score: 53%
• Precision Score: 99%
• Recall Score: 45%

Table 3: Analysis using ClusterCentroids

SMOTEENN Algorithm to Predict Credit Risk

• Accuracy Score: 65%
• Precision Score: 99%
• Recall Score: 62%

Table 4: Analysis using SMOTEENN

Ensemble Classifiers to Predict Credit Risk

1. BalancedRandomForestClassifier

• Accuracy Score: 79%
• Precision Score: 99%
• Recall Score: 87%

Table 5: Analysis using BalancedRandomForestClassifier

2. EasyEnsembleClassifier

• Accuracy Score: 93%
• Precision Score: 99%
• Recall Score: 94%

Table 6: Analysis using EasyEnsembleClassifier

Summary

For this analysis the recall metric is more important than the precsion metric. Compared to the ensemble classifiers to predict credit risk, the resampling models don't produce a perfect model fit. According to all the models the results of the EasyEnsembleClassifier reflect a high accuracy and a high recall value. I would recommend using this model.

Summary in a bulleted list

• Use Machine Learning to predict credit risk to produce quicker and reliable loan experiences
• Identify good candidates for loans lead to lower default rates
• Implement several machine learning models after preprocessing the data.
• Evaluate models to see how well they predict data