This project is about credit risk measurement for a bank. The project entailed a comprehensive analysis of client default tendencies relative to their backgrounds using advanced classification models, providing actionable insights by model comparison and refinement.
The project is organized into the following components:
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'credit_risk_dataset.csv': The dataset overall contains 12 columns with information about clients' backgrounds and some traits of their loans. There are 32573 data points in total.
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'Data Analysis.R': The R code contains the entire steps taken in the data analysis process.
Data Preprocessing - First, drop the rows with null values. Second, use synthetic data generation to deal with the unbalanced data. Third, apply correlation tests and principal component analysis.
Model Analysis - Conduct a suite of 5 classification models (Logistic Regression, KNN, SVM, Decision Tree, Random Forest) to the data with/without PCA, respectively.
Model Comparison - Compare the model performance based on various metrics (accuracy, sensitivity, specificity, ROC, AUC, F1-score).
Model Combination - Combine and improve the model by integrating the top 3 performing models using Boosting techniques. -
'Paper.pdf': The final paper "Classification Model Comparison and Improvement Regarding Credit Risk".
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'Slide.pdf': The slide presented at the Annual Conference of Financial and Banking Perspectives.
According to the model comparison, KNN has the highest sensitivity and Random Forest has the highest of both specificity and accuracy. By integrating the top 3 performing models using Boosting techniques, we finally construct a model with an accuracy rate of 93%.
If you have any questions, please contact [[email protected]].
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