Projects / Overview
Graph-Based Course Planning Platform
Engineered a graph-based course planning platform that uses Scrapy web scrapers to extract university course catalogs and models academic pathways in Neo4j. Built an interactive web application running optimized Cypher queries to map complex prerequisite networks, dynamically generate degree plans, and resolve scheduling conflicts.
ScrapyNeo4jCypherPython
Overview
Designed and developed an automated course planning ecosystem that transforms unstructured university catalog data into a connected graph network. The platform evaluates complex prerequisite trees and schedule constraints to generate conflict-free, personalized degree pathways for students.
Key Features & Engineering
- Automated Catalog Extraction: Built automated web scrapers using Scrapy to pull course descriptions, prerequisites, co-requisites, and scheduling constraints across university departments.
- Graph Database Architecture: Modeled course structures into nodes and relationships within Neo4j to efficiently represent multi-layered dependency networks and academic requirements.
- Optimized Pathfinding Queries: Wrote high-performance Cypher queries to traverse the graph, dynamically constructing completion paths and enforcing degree-specific rules.
- Interactive Planning Web App: Developed a user-facing application that dynamically generates degree schedules while detecting and resolving schedule conflicts and prerequisite gaps in real time.