Yelp Graph Analysis with EdgeDB

πŸ“˜ Description

This project explores how to build and analyze a graph-relational model using EdgeDB for real-world review data. It uses the Chicago Yelp dataset and focuses on businesses, users, and reviews to uncover trends in customer sentiment and reviewer behavior.

Part of the MSDS 420 course at Northwestern University, this assignment reinforces graph thinking and complex querying over nested data relationships using a modern, developer-friendly database system.

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πŸ”§ Features

  • EdgeDB Modeling: Constructs a schema with strongly typed relationships between businesses, users, and reviews.
  • Complex Queries: Includes multi-level filters, optional links, and aggregates for exploratory data analysis.
  • Graph Analysis: Reveals patterns in review count, business categories, and user ratings through relationship traversal.
  • Jupyter Notebook Interface: Code executed interactively for clarity and reproducibility.
  • Chicago Yelp Focus: All insights are grounded in real user activity within the Chicago metro area.

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πŸ’‘ Key Insight

Graph-based databases like EdgeDB allow for more natural modeling of review ecosystems. This project demonstrates how businesses, customers, and sentiment are tightly linked and can be explored through intuitive, expressive queries.

πŸ”— View the source code on GitHub