Credit Risk Classification

In this Challenge, we used various techniques to train and evaluate a model based on loan risk. We used 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.

Instructions

The instructions for this Challenge are divided into the following subsections:

• Split the Data into Training and Testing Sets
• Create a Logistic Regression Model with the Original Data
• Write a Credit Risk Analysis Report

Split the Data into Training and Testing Sets

1. Read the lending_data.csv data from the Resources folder into a Pandas DataFrame.
2. Create the labels set (y) from the “loan_status” column, and then create the features (X) DataFrame from the remaining columns.
3. Split the data into training and testing datasets by using train_test_split.

NOTE: A value of 0 in the “loan_status” column means that the loan is healthy. A value of 1 means that the loan has a high risk of defaulting.

Create a Logistic Regression Model with the Original Data

1. Fit a logistic regression model by using the training data (X_train and y_train).
2. Save the predictions for the testing data labels by using the testing feature data (X_test) and the fitted model.
3. Evaluate the model’s performance by doing the following:
   • Generate a confusion matrix.
   • Print the classification report.

Write a Credit Risk Analysis Report

1. An overview of the analysis: Explain the purpose of this analysis.
2. The results: Using a bulleted list, describe the accuracy score, the precision score, and recall score of the machine learning model.
3. A summary: Summarize the results from the machine learning model. Include your justification for recommending the model for use by the company.