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restaurant-data

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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

A SQL + visualization project analyzing India's restaurant landscape through the Swiggy dataset. Explores 61,425 restaurants across 8 cities using structured queries and presents the findings through an editorial-style interactive dashboard — built entirely in HTML, CSS, and JavaScript without any framework.

  • Updated May 28, 2026
  • HTML

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