Project case study

Retail association-rule engine

Large-scale association-rule analytics for discovering product, category, and department relationships.

Turned basket-level relationships into usable retail intelligence for planning and decision support.

Spark Python Power BI Airflow Apriori

Context

The problem

Retail teams needed a way to turn basket-level behavior into repeatable intelligence about which products and categories were commonly shopped together.

The core challenge was not just running Apriori at scale, but turning it into something repeatable and useful for business teams.

System trace

How the work moved through the system

A high-level operating path: where the request starts, how the system shapes it, and how other teams consume the result.

1
Spark and Python handled large-scale basket preparation and association-rule processing.
2
Airflow-style orchestration made recurring data preparation and reporting more reliable.
3
Power BI translated the discovered relationships into reviewable business output.

Method

Apriori

Used association-rule mining to identify product and category relationships from large retail datasets.

Delivery shape

Repeatable

Focused on a workflow that could be rerun and consumed by analysts rather than a one-off notebook result.

Architecture

System shape

1 Spark and Python handled large-scale basket preparation and association-rule processing.
2 Airflow-style orchestration made recurring data preparation and reporting more reliable.
3 Power BI translated the discovered relationships into reviewable business output.

Ownership

What I handled

1 Built data preparation flows for product, category, and department-level association analysis.
2 Turned Apriori output into reusable structures that could support planning and decision workflows.
3 Handled the engineering around scale, repeatability, and downstream consumption.

Lessons

What carried forward

1 Useful data science work often depends on making the run path repeatable before tuning the analytical method.
2 Retail insights become more actionable when users can move between product-level and category-level views.

Engineering decisions

Productize the analysis path

The key decision was to treat association-rule mining as an operational analytics product, not just a model run.

Expose relationships at multiple taxonomy levels

Supporting products, categories, and departments made the output useful to different retail planning conversations.

What can be shown

Public evidence without internal names

The internal systems stay private. This section keeps the public parts: my role, system boundaries, technology context, scale, decisions, constraints, and what I learned.

Internal enterprise system High-level architecture Scale signal

Method

Apriori

This case study can name the standard algorithm while omitting thresholds and client datasets.

Architecture shape

Basket and taxonomy data is prepared with Spark and Python.
Association-rule processing identifies product, category, and department relationships.
Orchestration and BI layers make the workflow repeatable and consumable.

Responsibilities

Built scalable preparation and association-rule processing flows.
Turned analytical output into reusable structures for planning and decision support.
Kept client data, product taxonomy, and commercial thresholds private.

Constraints

Client datasets, product identifiers, basket-level examples, and thresholds are confidential.
Site notes describe the analytical pattern without publishing outputs.

Supporting context

High-level architecture

High-level association-rule workflow

Can be represented as basket preparation, Apriori processing, relationship output, orchestration, and BI review.

Scale signal

Standard method context

Apriori is a public standard method; enterprise inputs and outputs remain private.

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Spark + Python + Data platform