Real-Time Product Price Scraping and Analysis

Abstract

This paper describes a real-time system for capturing and analyzing product prices from multiple e- commercewebsites.Inthecaseofe-commerceretailers,havinginstantaneousaccesstoonlinepricinginformation is essential for consumers, market analysts, and retailers because prices are subject to modification constantly owing to numerous factors like dynamic pricing, promotional offers, and competition. Our system incorporates advanced web scraping methods such as asynchronous data retrieval, proxy cycling, and headless browser automation, which make it possible to capture data reliably during real-time changes. The captured data is then refined for normalization and analyzed to establish statistical and machine-learning models that detect trends, price variations, and competition levels. The system\'s architecture is designed with high availability, fault tolerance,andextensibility,thusmakingitadaptabletolargerintelligentpricinganddecision-makingsystems.In addition, we discuss the legal and ethical issues of web scraping and analyze the performance of our system on real-world data sets.

Introduction

In the dynamic world of e-commerce, pricing plays a crucial role in shaping consumer behavior and maintaining competitiveness. Traditionally, pricing strategies were based on market trends, competition, and customer preferences. Today, companies increasingly rely on real-time product scraping and price analysis using automated tools to gather and analyze online data. This allows them to identify pricing trends, anomalies, and react quickly to market changes.

The system described leverages web scraping technologies such as Selenium, BeautifulSoup, and headless browsers (e.g., Puppeteer) to handle JavaScript-loaded content and extract key product information like name, price, and availability. It uses Python and is equipped with a graphical user interface (GUI) built with Tkinter, allowing users to input URLs, set scraping preferences, view real-time results, and receive alerts for price drops.

For visualization, it integrates Matplotlib to show historical pricing trends via charts. The system uses techniques like rotating user-agent headers and randomized delays to avoid detection and includes error handling for issues like page failures or internet loss. All data is stored locally (CSV or SQLite) and is lightweight enough to run on most computers without external dependencies.

Literature Survey

• Tools like BeautifulSoup, Scrapy, and Selenium are commonly used for web scraping.

• Headless browsers like Puppeteer are suited for dynamic content scraping.

System Features

• Real-time scraping and data analysis.

• GUI for user interaction and control.

• Automated background tasks using Tkinter.after() and Python threading.

• Visualization of price trends using embedded Matplotlib charts.

• Local storage of scraped data with options for alerts and logs.

Results

The system is accurate, fast, user-friendly, and effective for real-time price comparison across e-commerce platforms. It successfully maintains usability and responsiveness, making it a valuable tool for consumers and businesses seeking to track and respond to market price changes efficiently.

Conclusion

In this project, we whipped up a nifty little program that sits pretty on your desktop and can check out product prices on the fly. It\'s armed with Python, Selenium, BeautifulSoup, Tkinter, and Matplotlib to get the job done. It\'sgotaslickinterfacethat\'sano-brainertouse,hooksyouupwiththepowerofpullingdatafromwebsites,and even tosses in some fancy graphs for good measure. Pulling product info, making sense of it, and throwing it up on your screen with charts that move and shake that all shows this setup can hack it in the everyday world. Testsshowthissetupnailsitwithfastandright-on-the-moneydetails,likecostshowfolksrateit,andstraight-up links even on sites using fancy web code. Itallcomesdowntosimpleaccess,lettingthingsfunctionontheirown,andviewingreal-timedatafacts.

References

[1] Sakhare, Nitin & Verma, Devika & Kolekar, Vikas & Shelke, Avinash & Dixit, Akhilesh & Meshram, Nikhil. (2023). E-commerceProduct Price Monitoring and Comparison using Sentiment Analysis. International Journal on Recent and Innovation Trends inComputing and Communication. 11. 404-411. 10.17762/ijritcc. v11i5.6693. [2] F.Chen,\"ResearchonReal-timeE-commercePriceComparisonSystemUsingPythonWebScrapingTechnology,\"InternationalJournalof Computer Science and Information Technology, vol. 16, no. 1, pp. 75–89, 2024. [3] Shaikh, Arman & Khan, Raihan & Panokher, Komal & Ranjan,Mritunjay & Sonaje, Vaibhav. (2023). E-commerce Price ComparisonWebsite Using Web Scraping. International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences.Volume 11. 1-13. 10.37082/IJIRMPS.v11.i3.230223. [4] Saeed, Soobia, Mahmood Naqvi and Muhammad Ali Memon. “E-Commerce Web Crawling to Facilitate Consumers for EconomicalChoices.” (2020). [5] H.SandJ.Praveenchandar,\"AnEffectiveWebScriptingAlgorithmforRealTimeDataProcessing,\" 20242ndInternationalConferenceon Sustainable Computing and Smart Systems (ICSCSS), Coimbatore, India, 2024.

Copyright

Copyright © 2025 M. Anitha, T. Anusha, SK. Fathimabi, P. Durga Sowmya, N. Preethi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.