100 beginner machine learning projects with runnable Python code, tests and honest case studies. By Networkers Home, Bangalore.
This teaching project builds a simple, interpretable credit-scoring baseline using a synthetic credit-like dataset. It demonstrates careful handling of missing values, identification of a proxy feature, and a small subgroup fairness audit. The code trains a logistic regression (an interpretable linear model), evaluates calibration with the Brier score, inspects false-positive rates across groups, and emits a short educational model card describing review and appeal steps. The project is explicitly educational and not suitable for real lending decisions.
The dataset is synthetic and generated inside the code to mimic common credit-record features (age, income, employment length, credit history length, previous defaults) plus a categorical sensitive attribute used for subgroup analysis. The README documents provenance: the data are synthetic for classroom use only, not drawn from real credit records, and thus cannot represent repayment behaviour reliably. Students should note legal restrictions: any real credit scoring must comply with local financial regulation, data protection laws and non-discrimination rules. The synthetic data intentionally inject missing values and a proxy variable correlated with the sensitive attribute to demonstrate how proxy variables can unintentionally encode group information.
Run the project as a script: python project.py It prints a JSON object with evaluation metrics and a short model card.
Run pytest in the project directory: pytest -q The tests check that the run function returns the expected keys, that metrics are in sensible ranges, that the main held-out metric meets a conservative floor, and that the run is deterministic with the same seed.
Measured by running python project.py (seed 42) on 2026-10-23; the tests in test_project.py assert the floor.
| Metric | Value |
|---|---|
| auc | 0.8680 |
| brier | 0.0436 |
| threshold | 0.5000 |
| fpr_region_0 | 0 |
| fpr_region_1 | 0 |
| model_card | Model card - educational baseline. Model: LogisticRegression. Data: synthetic classroom data. This is not suitable for operational lending. Appeal: any adverse outcome described here would require human review, documented reason codes and an appeal process. Reviewers should check feature provenance, missing data handling and proxy correlations (for example the ‘proxy’ column). |
This project uses synthetic data only and is intended for classroom demonstration. Synthetic records do not capture real repayment dynamics or the institutional, behavioural and collection biases found in operational credit systems. The logistic regression baseline is interpretable but simplistic; production systems require rigorous validation, human oversight, anti-discrimination review, and legal compliance. Do not use the outputs for any actual credit decision-making.
Possible next steps include: replace synthetic data with a properly licensed real dataset; extend to causal and counterfactual analyses; perform larger fairness audits and calibration over score buckets; add thresholds and appeal workflows that involve human reviewers and logging for auditability.
Full case study, build steps and evaluation checks: Credit scoring model on networkershome.com
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