Credit Risk Analysis for Extending Bank Loans

This repository contains a Python script for credit risk analysis on a bank loan dataset. The goal is to build machine learning models to predict loan defaulters and aid decision-making for extending bank loans. The dataset used is available at Kaggle under the following link:

Credit Risk Analysis for Extending Bank Loans Dataset

The analysis includes

• Data cleaning
• Exploratory data analysis (EDA)
• Handling missing values
• Outlier detection
• Correlation analysis
• Variance inflation factor (VIF) calculation
• Class imbalance analysis

Libraries Used

The Python script uses the following libraries:

• numpy
• pandas
• plotly.express
• plotly.figure_factory
• statsmodels.api
• tabulate
• scipy.stats
• statsmodels.stats.outliers_influence
• sklearn.tree
• sklearn.inspection
• sklearn.ensemble
• sklearn.svm
• sklearn.linear_model
• sklearn.model_selection
• sklearn.metrics

Analysis Steps

The steps followed in the analysis are as follows:

• Load the dataset and inspect its structure.
• Check for missing values in the dataset.
• Calculate the correlation between different features.
• Calculate the Variance Inflation Factor (VIF) for the features to check for multicollinearity.
• Handle class imbalance in the target variable 'default.'
• Detect and handle outliers in the dataset.
• Split the data into a training set and a test set.
• Fit a Logistic Regression, Random Forest, and Support Vector Machine (SVM) models to the training data.
• Evaluate the models using various metrics, including accuracy, precision, recall, F1 score, and AUC-ROC score.
• Perform feature importance analysis using permutation importance.

Models Trained

The three models trained on the dataset:

1. Random Forest Classifier
2. Support Vector Machine (SVM)
3. Logistic Regression

              Model    Accuracy  Precision  Recall    F1 Score    AUC-ROC Score
        Random Forest  0.807143   0.720000  0.473684  0.571429       0.702528
                  SVM  0.864286   0.952381  0.526316  0.677966       0.758256
  Logistic Regression  0.850000   0.814815  0.578947  0.676923       0.764964