SCRAPPING DATA FROM GOOGLE MAPS USING PYTHON

Abstract

Web scraping has become an essential technique for collecting large-scale data from online platforms for research, analytics, and business intelligence. This project focuses on scraping publicly available business information from Google Maps using Python. Google Maps contains extensive data such as business names, addresses, contact details, ratings, reviews, and geographic coordinates, which can be useful for market analysis, competitor research, location intelligence, and trend analysis.The proposed system utilizes Python libraries such as requests, BeautifulSoup, and browser automation tools like Selenium to extract structured information from dynamically loaded web pages. Since Google Maps uses JavaScript to render content, automated browser control is implemented to simulate user interaction and retrieve dynamic data. The collected data is parsed, cleaned, and stored in structured formats such as CSV or Excel files for further analysis. The system emphasizes automation, scalability, and data organization. It also considers ethical and legal aspects, including compliance with website terms of service and responsible data usage practices. The extracted dataset can support various applications such as business directory creation, sentiment analysis, geographic data visualization, and machine learning models for predictive analytics.