Overview

This project aims to build a credit risk classification model using a dataset of historical lending activity from a peer-to-peer lending services company. The objective is to create a model that can accurately identify the creditworthiness of borrowers, distinguishing between healthy loans and high-risk loans.

Steps Taken

1. Data Preparation

   • Read the "lending_data.csv" data file from the Resources folder into a Pandas DataFrame.
   • Created the labels set (y) from the "loan_status" column, indicating 0 for healthy loans and 1 for high-risk loans.
   • Created the features DataFrame (X) from the remaining columns.
   • Split the data into training and testing datasets using the train_test_split function.

2. Logistic Regression Model

   • Developed a logistic regression model using the training data (X_train and y_train).
   • Applied the fitted model to make predictions for the testing data labels using X_test.

3. Model Evaluation

   • Evaluated the performance of the logistic regression model by performing the following steps:
     • Generated a confusion matrix to analyze true positives, true negatives, false positives, and false negatives.
     • Printed the classification report, including accuracy, precision, recall, and F1-score metrics.
     • Analyzed the model's ability to predict both healthy loans (0 labels) and high-risk loans (1 labels).

4. Credit Risk Analysis Report

   • Included a summary and analysis of the machine learning model's performance.
   • Described the accuracy score, precision score, and recall score using a bulleted list.
   • Provided a summary of the results and justified the recommendation for using the model by the company or provided reasoning if not recommending the model.

Conclusion

This credit risk classification project involved building a logistic regression model to identify the creditworthiness of borrowers using historical lending data. The project followed a step-by-step approach to split the data, train the model, evaluate its performance, and provide a credit risk analysis report. The model's performance metrics, including accuracy, precision, and recall, were analyzed and presented in the report, enabling a justified recommendation for the model's use or reasoning.