Data preparation
Financial and demographic variables were cleaned, checked, encoded, and prepared with a protected train, validation, and test workflow for an imbalanced default-classification problem.
Financial Analytics
Credit Risk Prediction
An end-to-end classification and reporting project covering preparation, imbalance-aware comparison, threshold selection, and credit-default risk communication.
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Why it matters
Frames model errors around missed defaults and unnecessary false alarms.
My role
Data scientist and analytical report author
- 01Cleaned financial and demographic variables, separated validation and test evidence, and prepared an imbalance-aware default-classification workflow.
- 02Compared Logistic Regression, Random Forest, and XGBoost with ROC, precision-recall, confusion matrices, F1, precision, and recall rather than relying on accuracy alone.
- 03Optimized the probability threshold around missed-default and false-alarm trade-offs, then produced a structured analytical report explaining evidence and limitations.
Baseline & model comparison
Interpretable Logistic Regression establishes the baseline before Random Forest and XGBoost test nonlinear relationships and feature interactions.
Imbalance-aware evaluation
Precision-recall curves, ROC curves, F1, class-level precision and recall, and confusion matrices expose performance on defaults instead of allowing the majority class to dominate the conclusion.
Threshold selection
The operating threshold is tuned on validation evidence to balance capturing defaults against unnecessary false alarms, then assessed on untouched test data.
Reporting value
The final report explains model trade-offs and limitations in decision language, demonstrating both analytical work and structured communication.
Technical implementation
How the solution was built.
Each layer connects an implementation choice to the decision or workflow it supports.
06 layers| Layer | Implementation | Operational purpose |
|---|---|---|
| Data preparation | Cleaning, encoding and validation of financial and demographic variables | Create a consistent modelling dataset while protecting test evidence |
| Class imbalance | Imbalance-aware training and evaluation focused on the default class | Prevent majority-class accuracy from hiding missed defaults |
| Model comparison | Logistic Regression, Random Forest and XGBoost | Compare an interpretable baseline with nonlinear ensemble models |
| Evaluation | ROC, precision-recall, F1, class precision, recall and confusion matrices | Measure both discrimination and the practical cost of classification errors |
| Threshold selection | Validation-based probability cutoff optimization | Balance missed defaults against unnecessary false alarms |
| Reporting | Structured analytical report covering method, evidence, trade-offs and limitations | Communicate model results in decision language rather than metric lists |
Product walkthrough
Screens connected to decisions.
report cover
Frames the analytical question and establishes the report as a decision-oriented deliverable.
project context
Explains the lending problem, business consequences, and scope of the modelling workflow.
model evaluation
Communicates model comparison, threshold trade-offs, and classification errors visually.