Devam Shah

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EDA

Food Delivery Market EDA

Exploratory analysis of restaurants across 98 Indian cities to understand what drives ratings, engagement and delivery adoption.

211,944restaurant rows across 98 cities (60,011 after cleaning)

View the code on GitHub ↗
Stack: Python, pandas, NumPy, Matplotlib, Seaborn

Live demo

A working version of this project will run right here.

Coming soon

Plan: Interactive charts built from pre-computed results, with filters by city and price range.

TODO: build and embed the demo

Key findings

  • Price does not predict rating.
  • More photos go with more votes.
  • Delivery is underused, which suggests a first-mover opportunity.
  • Price range 3 has the best rating-to-cost balance.
  • Popular restaurants are also well rated.

How it works

  • Raw data of 211,944 rows by 26 columns, cleaned to 60,011 rows by 16 columns.
  • Written to help both existing restaurant owners and new entrants make data-informed decisions.
  • For educational and informational purposes only.

Code and write-ups