Price Comparison & Price-Tracking Platform with AI Prediction

Abstract

The rapid growth of e-commerce platforms has provided consumers with multiple choices for purchasing products online. However, comparing prices across different websites manually is time-consuming and inefficient. This project proposes a Price Comparison and Price-Tracking Platform with AI Prediction that automatically collects product prices from multiple online stores and displays them in a single interface. The system uses web scraping techniques to extract product details from popular e-commerce websites such as Amazon, Flipkart, and Meesho. The collected data is stored in a MySQL/SQLite database and processed using machine learning algorithms to predict future price trends.The platform helps users make better purchasing decisions by showing real-time price comparisons, historical price charts, and price drop alerts. This system reduces the time spent searching for the best deals and improves the overall shopping experience.

Introduction

The AI-Based Price Comparison and Price-Tracking Platform is designed to simplify online shopping by allowing users to compare product prices across multiple e-commerce websites, such as Amazon, Flipkart, and Meesho, from a single platform. Since product prices frequently vary due to discounts and promotional offers, manually comparing prices is time-consuming. The proposed system addresses this issue by automatically collecting product information through web scraping and storing it in a database for analysis.

The platform integrates Artificial Intelligence (AI) and Machine Learning (ML) to analyze historical price data and predict future price trends. Based on these predictions, users can decide whether to buy a product immediately or wait for a price drop. The system also supports price tracking and sends price alerts when products become cheaper.

Research Objectives

• Compare product prices across multiple e-commerce websites.
• Automate product data collection using web scraping.
• Store price and product data in a structured database.
• Predict future prices using machine learning algorithms.
• Notify users about price drops through alerts.

Literature Survey

Existing price comparison systems mainly focus on collecting and displaying product prices but lack intelligent features such as price prediction and continuous price tracking. Recent studies have introduced web scraping and machine learning techniques, including Linear Regression, Decision Trees, and Time Series Forecasting, to improve price prediction and decision-making. However, many existing platforms still suffer from limited website coverage, inaccurate predictions, and difficulties with dynamic web pages.

System Architecture

The system follows a three-layer architecture:

• Frontend Layer: User interface for searching products and viewing comparisons.
• Backend Layer: Handles web scraping, price comparison, machine learning, and business logic.
• Database Layer: Stores product details, price history, and user information.

The software stack includes Python, Node.js, MySQL, BeautifulSoup, Selenium, Scrapy, TensorFlow, Visual Studio Code, and Postman.

Methodology

The platform collects product data using web scraping and APIs, cleans and organizes the data, and applies AI techniques such as machine learning and natural language processing (NLP) to identify matching products across different websites. It then compares prices, tracks historical price changes, predicts future trends, and generates price-drop alerts.

System Modules

The system consists of:

• User Interface for product search and comparison.
• User Management for registration and authentication.
• Web Scraping module for collecting product information.
• Database module for storing product and price history.
• Machine Learning module for price prediction.
• Price Comparison module for identifying the best available prices.

Results

The developed platform successfully provides real-time price comparison, price tracking, and AI-based price prediction. It significantly reduces the time users spend comparing prices manually and presents product details, prices, ratings, and availability in a single interface. AI and NLP techniques improve product matching accuracy, enabling users to make informed purchasing decisions while saving both time and money.

Conclusion

The AI-Based Price Comparison and Price Tracking System was developed to simplify the process of comparing product prices across multiple e-commerce platforms. The system successfully collects product information from different online stores such as Amazon and Flipkart and presents the comparison results in a clear and organized manner. This helps users quickly identify the best available price without spending time searching on multiple websites. The implementation of Artificial Intelligence techniques improves the accuracy of product matching and enables efficient data processing. The price tracking feature also allows users to monitor price changes and make better purchasing decisions. Overall, the system reduces manual effort, saves time, and enhances the online shopping experience. In conclusion, the project demonstrates how automation and AI technologies can be effectively used to build a smart price comparison platform. With further improvements and integration of advanced machine learning techniques, the system can become more powerful and widely useful for online consumers in the future.

References

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Copyright

Copyright © 2026 Sakshi Mohatkar, Gayatri Ambedwar, Isha Barbate, Ayush Thakre. 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.