SCRAPING DATA FROM GOOGLE MAPS USING PYTHON FOR MACHINE LEARNING

Abstract

Location-based data plays a crucial role in modern machine learning applications including retail analytics, route optimization, real-estate prediction, and geographic clustering. Google Maps is one of the richest geographic data sources; however, extracting structured information requires specialized automation techniques. This paper proposes a Pythonbased framework for scraping publicly available Google Maps data for machine learning (ML) tasks. The system incorporates web automation, HTML parsing, data cleaning, and feature engineering. Experiments demonstrate that the collected dataset enables improved prediction accuracy in ML models for business density estimation and location classification.