Skip to content
#

restaurant-data

Here are 34 public repositories matching this topic...

Exploratory Data Analysis (EDA) on Bengaluru restaurant data to uncover insights into ratings, cuisines, cost, location, and dining trends. Built using Python, Pandas, Seaborn, and Matplotlib to understand customer behavior and food business patterns.

  • Updated Jul 8, 2025
  • Jupyter Notebook

This project focuses on analyzing global restaurant data to uncover meaningful insights into customer preferences, pricing trends, and service availability. The dataset includes information such as restaurant names, locations, cuisines, ratings, price ranges, and services offered (e.g., online delivery, table booking).

  • Updated Sep 12, 2025
  • Jupyter Notebook

DoorDash scraper for Python and the command line: restaurants delivering in a US city, with rating, exact review count, cuisine, price band, address, delivery fee, delivery time, promotions and popular menu items with prices, as JSON or CSV. Free and open source; sign in with your QuanticData account, no proxies or headless browser to run.

  • Updated Oct 30, 2026
  • Python

Add this topic to your repo

To associate your repository with the restaurant-data topic, visit your repo's landing page and select "manage topics."

Learn more