DineIQ — Restaurant Analytics & Dashboard Integration

DineIQ Analytics — Restaurant Intelligence

A local competition/team project, not a paid client engagement or a live production deployment.

My contribution: full-stack integration, web dashboards, UI/UX and system architecture, as recorded in the team contribution document. The wider team worked on data engineering, machine-learning pipelines and advanced analytics. Development used AI assistance, documented in the repository.

The application combines a Django interface with PostgreSQL, Redis and Spark/Python analytics. It includes role-scoped dashboards, menu and customer analysis, filtering, reporting and review-only what-if insights.

The screenshot shows the local application using synthetic restaurant data. Revenue and profit figures are demo dataset values, not client results. Predictions and analytics are exploratory decision-support outputs; no business uplift or production accuracy is claimed.

This case study demonstrates dashboard integration and the presentation of complex analytical data in a usable web interface.