Türkiye Credit Score Model
A multidisciplinary data science project applying supervised machine learning to credit-risk analysis in a Türkiye-focused context.
A system designed around business clarity.
A multidisciplinary data science project applying supervised machine learning to credit-risk analysis in a Türkiye-focused context.
public
This case study focuses on process, architecture and methodology. Confidential business data is intentionally excluded.
02 · BUSINESS PROBLEM
What needed to change.
Credit-risk assessment requires the combination of multiple financial signals into a consistent predictive framework.
03 · SOLUTION
How the system responds.
A Python-based data preparation and XGBoost modeling workflow was developed to explore and predict credit-risk outcomes.
From source to business action.
01Financial Dataset→
02Data Preparation→
03Feature Engineering→
04XGBoost Model→
05Risk Score
01Structured preprocessing
02Feature engineering
03Gradient-boosted modeling
04Risk-score output
Interface and system views.
Designed to create measurable value.
01[Model Metric] validation score
02Reusable modeling workflow
03Explainable analytical structure