Credit-Risk-Classification

Table of Contents

• Background
• Objective
• Credit Risk Analysis Report
• Method
• References

Background

Use a dataset of historical lending activity from a peer-to-peer lending services company to build a model that can identify the creditworthiness of borrowers.

Objective

Using knowledge of Python and supervised machine learning to train and evaluate the best model to use based on loan risk, where a healthy loan status is 0 and a high-risk loan status is 1.

Credit Risk Analysis Report

Please see the file credit_risk_analysis_report.md for full details regarding the description of the analysis for the machine learning models, bulleted lists describing balanced accuracy scores, prediction and recall of all machine learning models and a summary of the results of the machine learning models with a recommendation on which model to use.

Method

Split the Data into Training and Testing Sets

1. Read the lending_data.csv data from Resources folder into a Pandas DataFrame.
2. Create labels set (y) from 'loan_status' column and create features (X) DataFrame from remaining columns.
3. Check balance of the labels variable (y) with value_counts function.
4. Split dataa into training and testing datasets by using train_test_split.

Create a Logistic Regression Model with the Original Data

1. Fit a logistic regression model using the training data (X_train and y_train).
2. Save predictions on testing data labels using the testing feature data (X_test) and the fitted model.
3. Evaluate the mode's performance by completing the following: a. Calculate accuracy score of the model. b. Generate a confusion matrix. c. Print the classification report.

How well does the logistic regression model predict both the 0 (healthy loan) and 1 (high-risk loan) labels?

According to the balanced accuracy score of 95%, the model is performing well; however, this is likely due to an imbalanced dataset. When analyzing the dataset, the number of healthy loans (low-risk) highly outweigh the number of high-risk loans. This indicates that the model is better at predicting a loan status as healthy in comparison to a loan status as high-risk. As the dataset is imbalanced, we need to further look at the oversampled dataset.

When analyzing both the balanced and imbalanced classification reports, the model is able to predict healthy loans 0 100% of the time, with 99% of those recalled correctly. The model is only able to predict high-risk loans 1 with a precision of 85%, and of those 85% correctly identified there is a recall rate of 91%.

Overall, precision and recall values are extremely improtant because the cost of misclassification can be high. A high precision score is important to minimize false positives, which can lead to a loss of potential customers. On the other hand, a high recall score is important to minimize false negatives, which can lead to significant financial losses.

Predict a Logistic Regression Model with Resampled Training Data

1. Use the RandomOverSampler module from the imbalanced-learn library to resample the data. Confirm labels have an equal number of data points.

2. Use the LogisticRegression classifier and resampled data to fit the model and make predictions.

3. Evaluate the model's perforamnce by completing the following: a. Calculate accuracy score of the model. b. Generate a confusion matrix. c. Print the classification report.

How well does the logistic regression model, fit with oversampled data, predict both the 0 (healthy loan) and 1 (high-risk loan) labels?

Overall, in analyzing the results of the machine learning models, the oversampled model would be the model of choice. The oversampled model generated a balanced accuracy score of 99%, which is an improvement than the prior logistic regression model on original data of 95%. It continues to perform the same when being able to correctly identify health loans, as the precision is 100%, and the recall remains at 99%. More importantly, where the oversampled model performs better is with the recall rates for high-risk loans. Although still obtaining a similar precision as the logistic regression model on original data at 84% in comparison to 85%, of these 84% that are correctly identified at high-risk, there is a recall of 99%. This is an improvement from the 91% on the prior logistic regression model on original data.

The oversampled model helps to better predict the true positives meaning that it is more effective at distinguishing high-risk loans with high recall accuracy. As well, as the number of false positives significantly decreases, the oversampled model is able to classify healthy and high-risk loans more correctly.

References

• Dataset provided by edX UofT Data Analytics, which had been generated by Trilogy Education Services, LLC. This is intended for educational purposes only.

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