credit-risk-classification

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

Open the starter code notebook and use it to complete the following steps:

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.

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. 3. Split the data into training and testing datasets by using train_test_split.

Create a Logistic Regression Model with the Original Data

Use your knowledge of logistic regression to complete the following steps:

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.

4. Answer the following question: How well does the logistic regression model predict both the 0 (healthy loan) and 1 (high-risk loan) labels?

Write a Credit Risk Analysis Report

Write a brief report that includes a summary and analysis of the performance of the machine learning models that you used in this homework. You should write this report as the README.mdfile included in your GitHub repository. Structure your report by using the report template that Starter_Code.zip includes, ensuring that it contains the following:

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: Summarise the results from the machine learning model. Include your justification for recommending the model for use by the company. If you don’t recommend the model, justify your reasoning.

Credit Risk Analysis Report

1. An overview of the analysis: Explain the purpose of this analysis. The purpose of this analysis is to create and evaluate the accuracy of a data model that predicts the credity worthiness of potential borrowers from peer-to-peer lending services.

2. The results: Using a bulleted list, describe the accuracy score, the precision score, and recall score of the machine learning model.

• Balanced Accuracy Score: 95% - This score indicates a high level of accuracy in predicting creditworthiness, taking into account both sensitivity (recall) and specificity. In this case, the model correctly predicted the outcome 95% of the time.
• Precision Score: 92% - This score measures how many of the predicted positive outcomes were correct. A precision score of 92% indicates that 92% of the time when the model predicted a borrower as creditworthy, it was correct.
• Recall Score: 95% - The recall score measures the proportion of true positive values correctly identified out of all actual positive cases. A score of 95% means the model is highly effective at capturing true positives.

3. Summarize the results from the machine learning model. Include your justification for recommending the model for use by the company. If you don’t recommend the model, justify your reasoning. Based on these results, I would recommend using this model for assessing the creditworthiness of borrowers in a peer-to-peer lending context. The high accuracy, precision, and recall scores demonstrate the model's effectiveness in predicting loan repayment outcomes. This level of accuracy is essential for risk assessment in lending, ensuring that lenders can make informed decisions to manage their capital flow and profitability effectively.