Real-Time Laptop Price Prediction through Web Data Analysis G. Anudeep Goud*, B. Tanishqi, B. Hemanth Reddy and G. Tanish Bhargav Department of IT, CMR College of Engineering & Technology, Kandlakoya, TS, India anudeepgoud29@gmail.com Abstract. The rapid rise of e-commerce is making price fluctuations a major challenge to consumers seeking to make informed purchasing decisions. In this study, we propose a machine learning-based laptop price prediction model, in conjunction with web scraping, that collects real-time price data from multiple e-commerce platforms. Based on key specifications such as processor, RAM, storage, GPU, and brand, regression models such as Linear Regression, Random Forest, and XGBoost Incorporated predict laptop prices. In previous studies, machine learning has been shown to be effective in price estimation, and web scraping has proven useful for real-time data collection. However, existing solutions often rely upon static datasets, inhibiting their ability to reflect market trends dynamically. The proposed system continuously updates its predictions through real-time price tracking with historical trend analysis, thus providing users with personalized price estimations and purchasing recommendations. The experimental results show that Random Forest outperforms traditional regression models and provides higher prediction accuracy. Being scalable, the system will be readily extended to other electronic products in the future. This research contributes to the field of e-commerce analytics through the presentation of a dynamic, data-driven approach to predicting laptop prices while increasing transparency and helping consumerism make cost-effective decisions. Keywords: Laptop Price Prediction, Machine Learning, Web Scraping, Regression Models, Real-Time Price Tracking, E-Commerce Analytics 1 Introduction Contrary to this benefit to consumers, e-commerce expansion has operated from advent to bring along some challenges of its own; such challenges include tracing prices and comparing the different changes across multiple platforms. It is the dynamistic nature that becomes an obstacle to making informed purchasing decisions with regards to extreme prices like laptops [1]. A combination of machine learning with web scraping seems to be a solution out of the many open-ended ones for the challenge. Web scraping is the automated extraction of information from different e-commerce websites, which allows comparing product prices and specifications in real-time across platforms [2]. © The Author(s) 2025 J. K. Katiyar et al. (eds.), Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025), Advances in Computer Science Research 124, https://doi.org/10.2991/978-94-6463-858-5_115 accuracy. 1384 G. A. Goud et al. Therein, it has been exploited to develop a price comparison system to help consumers whip up the best purchasing option [3]. Based on the information collected through web scraping, machine learning models are aimed at predicting price trends and for modeling. Regression algorithms, including Linear Regression, Random Forest, and XGBoost, have in recent times been actively involved in pricing prediction for some products based on historical data and product features [4]. The study tackling laptop price prediction trained its models to near-perfect accuracy using real-time scraped data from e-commerce sites [5]. Generally, the solutions developed over time so far are faced with some challenges like the constant rest of static datasets and very little constant updates in the aim to please trade dynamics [6]. This project thus includes the design of a dynamic laptop price prediction system that will combine real-time web scraping techniques with regression-motivated machine learning. This is intended to provide consumers with a price comparison system and some prediction, with two sides: price transparency and aiding consumers in making informed purchasing decisions [7]. The remaining sections will describe in detail the architecture of the system, including methods for data collection, model training, and implementation, along with experimental results that validate. 2 Literature Review Table 1. A summary of methodologies used in previous works Author (s) & Year Study Focus Algorithms/Techn iques Used Key Findings C. L. Reddy et al., 2023 [8] Laptop price prediction using real- time data Linear Regression, Random Forest; Web Scraping Combining real- time web scraping with regression- based machine learning algorithms for pricing results in high prediction Real-Time Laptop Price Prediction through Web Data Analysis 1385 N. Sanjana et al., 2022 [9] Laptop Price Prediction Using Machine Learning Algorithm s Decision Trees, Support Vector Machines, Gradient Boosting(Ensemble ) Ensemble methods, especially Gradient Boosting, showed better performance, highlighting the importance of choosing the right algorithm. A. Balti et al., 2023 [10] Predicting Laptop Prices in the Tunisian Market Using Data Mining and ML Supervised ML Techniques; Gradient Boosting Achieved 94% predictive accuracy by examining local market trends and important factors like brand and processor type. V. Prasad et al., 2024 [11] Predictive Modelling and Dynamic Analysis of Price Trends in E- Commerce Regression Models, Time Series Analysis, Supervised ML Stressed the significance of ongoing data collection and regular model updates to effectively forecast price trends in a dynamic environment. 3 Proposed System The proposed system aims to develop an intelligent laptop price prediction model using web scraping and machine learning techniques. The system extracts real- time laptop price and specification data from various e-commerce platforms and uses machine learning models to predict prices based on user-specified configurations. This system is further deployed as a web-based application, allowing users to input their preferred specifications and receive instant price predictions. 1386 G. A. Goud et al. 3.1 System Architecture 1. Client a. The user initiates the process in the Main Website by searching for some specific laptop or product. It is the front-facing interface through which the user interacts with the system. 2. Main Website a. The main website acts as a bridge connecting clients with the backend services of the system. b. It receives requests from the client for a price and sends the product query to the rest of the system. c. It processes and collates the information and provides the price as fetched on the main interface. d. It also interacts with the database in storing and retrieving information on laptops, products, and past queries. 3. Database a. The database consists of all the necessary data concerning products, prices, and scraped data from e-commerce websites. b. Once a client queries for a product, the main website fires a query to the database checking against previously stored information. c. If the needed information is not available on the database or is outdated, it activates the web scraper to refresh the price. 4. Web Scraper to Fetch Price a. The web scraper is able to instantaneously collect data from different E-commerce sites. Fig. 1. Client Input Interface b. When a price is demanded by the main website, the web scraper performs real-time querying against the appointed E-commerce sites for product detail and pricing. c. It is provided with the information and then relays this back to the main website for display to the client. 5. E-Commerce Websites a. These are the other websites (such as Amazon, Flipkart, etc.) where the web scraper pulls product information. b. It collects details like the price, specifications, and availability so it can provide accurate up-to-the-minute information back to the user on the main website. Real-Time Laptop Price Prediction through Web Data Analysis 1387 Fig. 2. System Architecture 3.2 Features of Proposed System 1. Regression models were used for price prediction in a more accurate way. expansion to other electronic products apart from laptops is scal 2. 3. The able. Web scraping helps to collect real-time data so that it could offer dynamic price prediction rather than static pricing comparisons. 4. Instead of general pricing trends, the model produces customized price estimates against certain laptop configurations such as processor type, RAM, storage, and GPU, subsequently increasing the accuracy of prediction in relation to the user's preferences. 4 METHODOLOGY 4.1 Collection and Storage of Data 1. The raw data, by this time, gets pre - processed for the sake of uniformity. 2. Going for missing value - handling and deduplication tasks. 1388 G. A. Goud et al. BeautifulSoup and Selenium are some of the web-scraping tools that fetch data from various e-commerce portals such as Amazon, Flipkart, and Best Buy, and data is gathered in real-time[12][13]. Scheduled automated scraping tasks would update and ensure the accuracy of periodic data collected. Entries gathering specifications of the laptop, pricing, and feedback are stored in databases like MySQL or PostgreSQL. 4.2 Data Preprocessing and Feature Engineering 3. Uniform standardization of specifications (like RAM, storage) across brands. 4. Conversion of currencies and pricing standardization. 5. Extraction of features from the text reviews and ratings could improve the model's performance. Fig. 3. Dataset Information 4.3 Machine-learning and Prediction Real-Time Laptop Price Prediction through Web Data Analysis 1389 Supervised models such as Linear Regression, Random Forest, or XGBoost have been trained to predict prices of laptops[14]. Time-series forecasting methods ARIMA or Facebook Prophets are investigated to understand the price fluctuations. Some analysis proceeded to search the effect of the consumer reviews over the pricing[15]. 4.4 Visualization, Deployment, and Model Improvement Visualization comes by means of interactive dashboards and graphs representing change in prices, value comparisons, and predictions[16][17]. The inputs from users will be considered to improve the model and enhance the predictive ability of them. The deployment allows for perfect scalability, reliability, and end-user access. This system is further deployed as a web-based application, allowing users to input their preferred specifications and receive instant price predictions[18]. 5 Result The price predictor for laptops proposed will be tested with real-time data culled from various e-commerce platforms. The machine learning models received training on the specifications that were extracted from shopping information such as processor, RAM, storage, GPU, and brand to predict prices. The Random Forest Regressor yielded the poorest error parameters, such as MAE and RMSE, among other models. Therefore, it can adequately provide accurate price predictions based on user-defined configurations while adapting to market fluctuations through continuous data updates. Fig. 4. Predicted Output Display A visual representation (Fig. 4) illustrates the expected output for my laptop price prediction project. It is a web-based interface in which users may input 1390 G. A. Goud et al. specifications, such as DDR version, operating system, RAM size, screen size, and any other key features. The interface includes drop-down menus and radio buttons for selection, followed by a Predict button, which would yield a forecasted price of the laptop. After submission, the estimated price appears in a green-highlighted box at the bottom, indicating the system's prediction based on the user's specifications. The accuracy of our project would be 90.21%. The web- based application will efficiently process user inputs and alter requests in real- time, which is commercial use of the application by consumers. 6 Conclusion and Future Scope This is a study that successfully developed a dynamic price prediction system which integrates web scraping with machine learning to estimate laptop prices based on real-time data. The system increases the transparency of pricing for the consumer and enables them to make informed decisions when purchasing a gadget. Using regression models provides higher accuracy than normal static price comparison tools. Data collection in real-time and advanced predictive modelling have been shown to cause significant improvements in price estimation reliability. Future work could explore deep learning models, work on the inclusion of more product categories and address the question of intelligent optimization of processing data. 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