DineIQ Analytics Uses Apache Spark and ML to Transform Restaurant Data Into Profit Insights

A student team from TechWiz Data Science Arena built DineIQ Analytics, a web-based restaurant intelligence platform designed to move beyond traditional spreadsheet-based reporting. The platform uses Apache Spark, PySpark, and Spark SQL to process large volumes of restaurant data including orders, pricing, promotions, ratings, and food wastage. Two independent machine learning pipelines — one built with Spark MLlib and another with Scikit-learn and XGBoost — solve the same analytical problems separately, and their outputs are compared for reliability. Every menu item is classified across multiple dimensions as a Profit Driver, Volume Driver, Hidden Opportunity, or Low Performer, with evidence-backed recommendations ranked by priority. The team also built a custom synthetic dataset generator to simulate realistic restaurant business data, as no suitable public dataset existed for the problem.