Television Price Prediction Through Web Scraping From E-Commerce Sites

DOI

10.2991/978-94-6463-858-5_116How to use a DOI?

Keywords

Television; Machine Learning Algorithms; Beautifulsoap and Selenium; Web Scraping; Ecommerce Sites

Abstract

In the current digital market, what buyers and sellers ought to know is the cost of consumer electronics, particularly televisions. With online retailing getting bigger and bigger, data available on the web can be utilized for finding pricing trends, customer preferences, and market dynamics. This study aims to develop an efficient predictive model for television pricing by web scraping, data preprocessing, feature engineering, and machine learning techniques. Web scraping, the extraction of television data from different e-commerce websites, is where the project starts: Python has some handy web scraping tools called BeautifulSoup and Scrapy. The data we collect includes very significant attributes like brand, size, resolution, type of display technology (LED, OLED, and QLED), refresh rate, smart features, customer ratings, and pricing history. Sentiment analysis on customer reviews is also performed to get a pulse on consumer sentiment that might affect pricing. Once the data has been collected, preprocessing techniques like dealing with missing values, identifying outliers and feature encoding, will be used to ensure that the data is coherent with regards to usability. Feature selection methods will find out the most influential features with respect to price. Second, several machine learning algorithms like gradient boosting, decision trees, random forests, and linear regression are applied to develop models predicting television prices. Lastly, 89% accuracy is attained. Cross-validation and regression analysis are applied to measure the performance of the models and verify their validity.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Katteboyenasuneetha
AU  - Shiva Shankar
AU  - Sameer
AU  - Akhil
PY  - 2025
DA  - 2025/11/04
TI  - Television Price Prediction Through Web Scraping From E-Commerce Sites
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 1393
EP  - 1404
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_116
DO  - 10.2991/978-94-6463-858-5_116
ID  - 2025
ER  -