This page sets out the dataset, the build steps, the metrics a credit model is judged on, the mistakes to avoid and the right scope for each level.
- Open data: the UCI Default of Credit Card Clients set has 30,000 records and 23 features, collected in Taiwan from April to September 2005.
- The output is a probability, so check calibration as well as ranking.
- Every decision needs a reason. Use coefficients or SHAP values to give the top factors for each applicant.
- Add a fairness check. The data includes sex, education, marital status and age.
- Logistic regression is the baseline that boosted trees have to beat.
Which dataset should you use for loan default prediction?
Start with Default of Credit Card Clients in the UCI Machine Learning Repository. It is free to use under a CC BY 4.0 licence and is cited in many papers. It records card repayment, not a loan application, so say that plainly in your report.
| What | Detail |
|---|---|
| Size | 30,000 records and 23 features |
| Where and when | Card holders in Taiwan, April to September 2005 |
| Features | Credit limit, sex, education, marital status, age, six months of repayment status, bill amounts and payment amounts |
| Target | Whether the holder defaulted on the next month’s payment |
| Licence | CC BY 4.0 |
Figures are from the UCI dataset page. If your guide wants loan applications, the Home Credit Default Risk competition on Kaggle is the common larger choice.
How do you build a loan default model step by step?
- Read the data dictionary. Know what each repayment-status code means before you plot anything.
- Split with stratification, so the share of defaulters is the same in training and test data.
- Engineer a few honest features: the ratio of bill to credit limit, the share of the bill paid and the number of late months.
- Fit logistic regression as the scorecard-style baseline.
- Fit a gradient-boosted model and tune it with cross-validation.
- Calibrate the probabilities with Platt scaling or isotonic regression, and draw a reliability curve.
- Explain and audit. Give the top reasons per applicant with SHAP, then compare error rates across groups with Fairlearn.
- Build the demo: a form that returns the default probability, a risk band and the three main reasons.
Which metrics should a loan default project report?
- ROC-AUC and the KS statistic, the two ranking measures credit teams use most.
- Recall and precision on defaulters at the cut-off you propose.
- Brier score and a reliability curve, because a predicted 20% should mean about one default in five.
- A fairness measure, such as the gap in approval or false-rejection rates between groups.
Defaulters are the smaller class, so accuracy alone flatters a model that approves everyone.
What mistakes cost marks in a loan default project?
- Calling it loan data when it is card data. Name the dataset correctly.
- Treating raw model scores as probabilities. Boosted trees and SVMs often need calibration.
- Using sensitive fields without comment. Examiners ask about bias; have the numbers ready.
- Tuning on the test set. Choose features, hyperparameters and the cut-off on validation folds.
- No cost view. A missed defaulter and a rejected good borrower do not cost the same.
How does the scope change for Diploma, B.Tech and M.Tech?
| Level | Scope | What to show |
|---|---|---|
| Diploma | Charts of default against age and credit limit, then logistic regression against a decision tree | A form that returns low, medium or high risk |
| B.Tech / B.E. | Feature engineering, boosted trees, calibration, SHAP reasons and a fairness report | A web app with the probability and the reasons |
| M.Tech / M.E. | A base paper reproduced, plus one extension: fairness-constrained training, reject inference, or deep tabular models against boosted trees | Results across folds with a significance test, and a paper in IEEE format |
Our CSE list has a related idea, a credit-risk model with a fairness audit. For M.Tech scope, read how to select a base paper first.
What goes in the report, and which viva questions come up?
Write the report around the lending decision: the problem, the data and its limits, the method, the results, the fairness findings and what a lender should do with the score. Our report and PPT help covers the document and slides. Prepare for these:
- Why is logistic regression still used in credit scoring?
- What does calibration mean, and how did you test it?
- What is the KS statistic?
- Did your model treat men and women differently, and how do you know?
- How would you set the approval cut-off for a real lender?
Other tabular guides: credit card fraud detection and customer churn prediction.
This guide is a plan to discuss with your guide, and the figures in it come from the linked dataset pages and papers, not from our own work. Our 8 delivered case studies are M.Tech and M.E. projects, each shown with its real paper pages, screens and results.
Frequently asked questions
Which dataset can I use for loan default prediction?
The UCI Default of Credit Card Clients dataset is open and well documented: 30,000 records from Taiwan with 23 features covering the credit limit, repayment status, bill amounts and payments from April to September 2005. Kaggle’s Home Credit Default Risk competition data is a larger alternative.
Which model is best for loan default prediction?
Logistic regression is the standard in credit scoring because every coefficient can be explained. Gradient-boosted trees usually separate defaulters a little better. Train both, compare them on the same folds, and report how well calibrated each one’s probabilities are.
Why does a loan default model need a fairness check?
The data includes sex, education, marital status and age, and a lending decision affects people’s lives. Measure whether approval and error rates differ between groups, report it, and show what changes when the sensitive fields are removed or a fairness constraint is added.
What is the difference between loan default and credit card default data?
Both record whether a borrower missed repayment. The UCI data is about credit card payments in the following month, while loan data describes an application and a loan’s whole term. The modelling steps are the same, so state clearly which one your project uses.
Can The Ultimate Project World help with a loan default prediction project?
Yes. In the free consultation, share your level, your deadline and anything your guide has already approved. We will tell you exactly which parts we can take on, such as the code, the report, the PPT and viva preparation.
