Food delivery analytics

Understanding orders & revenue trends (2017)

Goal: Clean a raw food delivery dataset and extract actionable insights on revenue, order volume, and demand peaks across time (hour/day/month).

What we did: Data cleaning, feature engineering (date/time features), KPI computation (total revenue, average basket), and simple visualizations to highlight seasonal patterns and peak ordering hours.

Outcome: A clear dashboard-like summary of when and how revenue is generated, with business recommendations (staffing, promos, operational timing).

Raw Dataset Overview

Raw dataset snapshot before cleaning: columns include IDs, dates, totals, currency, and operational fields. This step documents the initial structure before selecting relevant variables and creating analysis-ready features.

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