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Basketball Graph Data Analysis using CQL

  • Tech Stack:
    • Language: Python, Cypher Query Language
    • Packages: Neo4j
  • Github URL: Project Link

This dataset is well-structured for creating a Neo4j graph database representing players, teams, coaches, and games. It establishes relationships such as teammates, coaching, plays-for, and played-against, making it rich for queries. I perform a series of graph data analysis using Cypher Query Language (CQL) to extract insights and visualize patterns in basketball data. The project demonstrates the power of graph databases for sports analytics, offering a unique perspective on player performance, team dynamics, and game strategies.