Classification Metrics
Model EvaluationFrom a confusion matrix, computes the metrics that actually matter - accuracy alone hides a lot on imbalanced data (e.g. rare defect classification).
Enter confusion matrix values and calculate.
← All Domains
Domain · Areas of Expertise
AI & Machine Learning
Applied ML for Industrial Engineering - prediction, classification, and pattern detection on shop-floor and operations data.
I teach AI & Machine Learning alongside Operations and Analytics, so the framing here stays applied: not ML for its own sake, but ML as another tool in the same box as SPC, DMAIC, and forecasting - for predictive maintenance, quality classification, and demand prediction.
Hands-on Tools
Core ML mechanics, worked by hand
Small, transparent implementations of the algorithms behind the black box - useful for teaching, and for sanity-checking a model's output.
Simple Linear Regression
PredictionLeast-squares fit of y on x, with R² and a point prediction - the same idea behind more complex forecasting models, worked by hand.
Enter paired X/Y data and fit.
Neuron Activation Explorer
Neural NetworksGiven a weighted sum z, see how the standard activation functions transform it.
Enter z and compute.
Predictive Maintenance: RUL Estimator
Industrial ApplicationA linear degradation model estimating Remaining Useful Life from a sensor trend - the simplest version of the models behind condition-based maintenance.
Enter sensor trend and estimate.
K-Means: One Iteration, Worked by Hand
ClusteringAssigns each point to its nearest centroid, then recomputes the centroids - the two-step loop that k-means repeats until convergence.
Enter points and centroids, then run.
Custom GPTs Built
Purpose-built AI assistants, in active use
Five custom GPTs designed and configured for specific recurring problems in teaching, quality operations, and student entrepreneurship - each scoped to one job rather than being a general-purpose chatbot.
Q360 - Quality Operations Copilot
An AI copilot for quality operations - audit prep, SOP interpretation, and root-cause guidance framed around ISO and Six Sigma quality workflows.
Open GPT ↗DOIE OBE Blooms Questions Builder
Generates outcome-based-education exam questions mapped to Bloom's Taxonomy levels for Department of Industrial Engineering courses.
Open GPT ↗Nara's Startup Analyst AI
Analyzes student startup ideas and pitches - market sizing, viability checks, and structured feedback for venture mentoring.
Open GPT ↗DOIE Industrial Engineering Mentor
A course-aware mentor for Industrial Engineering students - answers concept questions and points to the right technique for a given problem.
Open GPT ↗DOIE Startup Journey - Nara's 1.0
Guides students step-by-step through an entrepreneurship journey, from idea validation to pitch-readiness.
Open GPT ↗Applied Focus
Where this shows up in my teaching & toolkit
AI & ML sits alongside Business Analytics and Operations in what I teach, backed by working proficiency in Python, SQL, Power BI, and generative AI tools - used for the same kind of decision-support work as the statistical and optimization tools elsewhere on this site.
AI & Machine Learning Course
Covers the mechanics behind these tools - regression, classification, clustering, and neural network basics - framed around Industrial Engineering use cases rather than generic examples.
Python, SQL & Analytics Toolchain
Data Analytics and Power BI certified, with working Python/SQL for the same data pipelines that feed the ergonomics, quality, and supply chain research on this site (see Certifications & Skills).
ML as the Next Layer on Classical IE Tools
Predictive maintenance extends OEE/reliability thinking (Work System Design); classification metrics extend SPC (Quality Management); regression extends demand forecasting (Supply Chain Analytics).