Intelligent Online Shopping using ML-based Product Comparison Engine

A relatively recent technique for gathering data online is known as scraping. The automated process involves the exploration of e-commerce websites and obtaining certain data, such as pricing, reviews, quality features, etc. Sentiment analysis of product attributes on e-commerce platforms, such as pricing, reviews, quality, etc., can substantially increase user satisfaction in the e-commerce industry. It continues to be difficult to envision precise sentiment analysis. This research work summarizes the product comparison website that implements intelligent web scraping. The website has a processing model that uses the Machine Learning (ML)-based product comparison engine. The simulation analysis for model training and testing uses the GitHub dataset and the Support Vector Machine (SVM) algorithm. The SVM classifies the product from various websites based on the rank value, assigned based on the selected features of the product. The proposed model is compared with other ML algorithms such as Decision Tree, Naïve Bayes, and Random Forest. The comparative analysis shows that the SVM algorithm can classify the best products with the highest accuracy ratio of 94.71%over other algorithms.