ML Based Scraper To Navigate Through Search Queries, Extracts Relevant Listings, and Data Such As Business Names, Categories, Addresses, Phone Numbers, Ratings, And Review Counts.

Abstract

The exponential growth of location-based services has made geographic and businessrelated data crucial for a wide range of applications, including market analysis, travel planning, and service optimization. Google Maps, one of the most comprehensive mapping platforms available today, contains a vast repository of realtime, location-specific data such as business names, ratings, addresses, and user reviews. This project focuses on programmatically accessing such data using Python, not through web scraping, which violates Google's Terms of Service, but by leveraging the official and authorized Google Places API. Through the API, developers can legally and efficiently retrieve structured data about places, including restaurants, hospitals, tourist spots, and more, using simple HTTP requests. The project involves integrating the API with Python to send queries, parse JSON responses, and extract relevant fields such as name, location, rating, and user reviews. The retrieved data can then be stored in databases or visualized for further analysis. By adhering to legal and ethical data usage practices, this project highlights the importance of responsible data access while demonstrating practical skills in Python programming, RESTful APIs, and JSON data handling. Additionally, alternative data sources like OpenStreetMap and Foursquare are explored for open and free access to similar geographic datasets.