Credit Risk Classification

Overview

The goal of this project is to assess the creditworthiness of borrowers using a logistic regression model, based on historical loan data. The dataset includes financial information such as loan status (0 for healthy loans and 1 for high-risk loans) along with other details about the borrowers and their loans. The main objective is to predict whether a loan is healthy or poses a high risk of default.

Dataset

The dataset includes the following financial information:

• Loan Status: Binary classification (0 for healthy, 1 for high-risk).
• Features: Borrower information including loan amount, annual income, debt-to-income ratio, and credit score.

The model was designed to predict the loan status based on these features to identify high-risk loans.

Machine Learning Process

1. Data Preprocessing:

   • The dataset was split into features (X) and target (y), with loan status being the target.
   • Data was then split into training and testing sets using train_test_split.

2. Model Training:

   • A logistic regression model was created using the training data.
   • The logistic regression model was chosen due to its efficiency in binary classification tasks.

3. Model Prediction:

   • After training, the model was used to predict the loan status on the testing data.

4. Model Evaluation:

   • The model was evaluated using various metrics such as accuracy, precision, recall, F1-score, and confusion matrix.

Results

Logistic Regression Model:

• Accuracy: 99%
• Precision:
  • Healthy loans (0): 1.00
  • High-risk loans (1): 0.86
• Recall:
  • Healthy loans (0): 0.99
  • High-risk loans (1): 0.94
• F1-Score:
  • Healthy loans (0): 1.00
  • High-risk loans (1): 0.90

These results indicate that the model is highly effective in predicting healthy loans and has a good balance of precision and recall for high-risk loans.

Confusion Matrix & Classification Report

Below is the classification report generated from the logistic regression model:

Conclusion

The logistic regression model performs extremely well for both healthy and high-risk loans. It achieves almost perfect accuracy, precision, and recall for healthy loans and performs strongly on high-risk loans with minimal misclassification.

Given its robust performance, this model is recommended for credit risk classification. The high recall for high-risk loans ensures that the model minimizes missed high-risk loans, which is crucial in preventing financial losses.

Resources

Here are the key libraries and resources used in this project:

• Pandas – for data manipulation and analysis.
• NumPy – for numerical computations.
• Scikit-learn – for machine learning models and evaluation metrics.
• Pathlib – for file system path manipulation.

Files in Repository

• credit_risk_classification.ipynb: Jupyter notebook containing the full analysis and code.
• lending_data.csv: The dataset used for training and testing.
• Images/classification_report.png: Visual representation of the classification report.