This page covers the core ML systems in the portfolio: churn prediction, inventory demand forecasting, credit risk prediction, and the observability layer used to keep model behavior visible after deployment.
Applied ML systems presented as complete workflows, with business objectives, modeling logic, evaluation discipline, and operational visibility.
Each case study should feel like a real deployable system, not a standalone notebook or isolated algorithm demo.
Clear targets, interpretable outputs, measurable performance, and artifacts that can support product, analytics, and operations teams.
These systems cover customer behavior prediction, inventory planning, credit risk estimation, and monitoring patterns for models in operation.
Supervised learning pipeline to identify at-risk customers using behavioral, transactional, and engagement features. Designed for proactive retention campaigns and uplift measurement.
Time-series and feature-enriched forecasting workflow to optimize inventory levels, reduce stock-outs, and support multi-horizon planning decisions.
Binary classification system for credit default risk using borrower and loan attributes, with probability-based risk bands, explainability, and observability-compatible outputs.
The observability layer brings together metrics, feature importance, ROC views, confusion matrices, and forecast tracking so model behavior remains legible over time.
The goal is not only to train models but to surface the right evaluation and monitoring views so issues become visible early and decisions can be explained clearly.
Classification performance visualized beyond simple accuracy numbers.
Error structure inspection to support operational threshold tuning.
Interpretability artifacts that connect predictions back to meaningful drivers.
Trend and horizon-aware views for planning and anomaly inspection.
This section is intended to host an interactive churn prediction experience where users can adjust feature values and observe model predictions and explanations in real time.
Interactive feature controls, predicted churn score, explanation panel, and a compact product-style response surface.
UI placeholder only. Model inference and backend integration are intentionally deferred to a later iteration.
It turns the page from a static ML showcase into an interactive demonstration of model-driven product UX.
Although the use cases differ, the ML systems share a common design vocabulary around supervised learning, probabilistic outputs, operational metrics, and interpretable artifacts.
This page focuses on the supervised ML side of the portfolio. Continue into NLP tooling, open the RAG assistant, or browse projects to see how the full AI system layers connect.