Proven
The public repository shows target framing, preprocessing, model comparison, holdout evaluation, local FastAPI serving, tests, reports, and model-card style limits.
Back to Work
Data / ML product proof
E-commerce Purchase Intention MLOps
A data/ML product proof around predicting online purchase intent from visitor-session behavior using a reproducible local MLOps workflow.
Direct answer
What makes this ML work public proof instead of a product claim?
The useful claim is proof of a reproducible ML workflow and product-decision framing, not proof of a live commercial personalization system.
Not proven
It does not prove commercial deployment, customer data, live personalization, automated intervention, revenue impact, production monitoring, or a tuned decision policy.
Public source: E-commerce Purchase Intention MLOps public repository
What this is
A data/ML product proof around predicting online purchase intent from visitor-session behavior using a reproducible local MLOps workflow.
Why it matters
Purchase-intent prediction is useful only when the target, features, evaluation, threshold costs, and product decision are clear enough to inspect.
What Navid explored
Navid framed the target, built the preprocessing and training path, compared Logistic Regression and Random Forest baselines, documented tradeoffs, exposed a local prediction API, and kept the public claim grounded in proof scope.
What it proves
It shows business-to-data translation: turning session behavior into features, evaluating a model, documenting the proof path, and asking what decision the prediction could improve before any intervention is automated.
What it does not prove
It is not client work, a deployed commercial model, a live personalization system, tuned threshold policy, production monitor, or proof of business impact.
Direction
It connects data science to product-system thinking: prediction is downstream of target design, evaluation, threshold cost, and product use.
Methods
What this work exercises.
- business-to-data
- reproducible ML workflow
- evaluation and threshold reasoning
- FastAPI model serving
- model-card style documentation
- proof-boundary discipline
Patterns
Reusable patterns.
Threshold Cost Before InterventionConnect model thresholds to false positives, false negatives, and the cost of acting before treating prediction as a product decision.Prototype Stays PrototypeKeep proof records, product studies, evaluation labs, and paused directions labeled honestly until the next proof gate is earned.Public Proof Before Product ClaimShow the smallest approved artifact, source, status, and boundary before using stronger product language.
Related notes
Thinking around the work.
NoteBusiness → Systems → Data → AI → ProductsAI is stronger when it is downstream of business context and system design.NoteWhen a prototype should stay a prototypeA prototype is useful when it clarifies what still has not been proven.NoteFrom demo to real workThe practical question is what the system reads, changes, owns, refuses, and leaves to people.
Referenced by
Where this work appears.
notesFrom demo to real workRelated notenotesPublic proof over personal brandingRelated notenotesWhen a prototype should stay a prototypeRelated notenotesBusiness → Systems → Data → AI → ProductsRelated notepatternsThreshold Cost Before InterventionPattern where this work appearspatternsPrototype Stays PrototypePattern where this work appearspatternsPublic Proof Before Product ClaimPattern where this work appearstopicsDecision SupportTopic connected to this worktopicsML Product ProofTopic connected to this worktopicsBusiness To DataTopic connected to this worktopicsThreshold CostsTopic connected to this worktopicsPrototype BoundariesTopic connected to this worktopicsPublic ProofTopic connected to this worktopicsAI StrategyTopic connected to this worktopicsProduct SystemsTopic connected to this workreadingPathsInspect Public ProofReading path includes this workreadingPathsML Product DecisionReading path includes this work
Next step
For data or ML product conversations, start with the decision the prediction should improve, the cost of wrong predictions, and the evidence needed before shipping.
Contact
Send the working context.
Send the business pressure, workflow, source boundary, or proof question. Best fit: hiring, AI roadmap, product-system work, and collaboration where evidence matters before claims.
Navid