Abstract
Abstract: The rapid proliferation of electronics devices has led to an overwhelming number of options available to consumers, making it challenging to identify the most suitable products based on individual preferences. In this paper, we propose a novel recommendation system leveraging machine learning techniques to assist users in making informed decisions when purchasing electronics devices. Our system utilizes a collaborative filtering approach combined with feature engineering and natural language processing to analyze user preferences and product characteristics. We present the design, implementation, and evaluation of the recommendation system, demonstrating its effectiveness in suggesting personalized electronics device recommendations. Through experimentation on a realworld dataset, we showcase the system’s ability to accurately predict user preferences and provide relevant product recommendations, thereby enhancing the overall shopping experience for consumers in the electronics domain. Keywords — machine learning , database classification ,data collection,and algorithm Keywords– use rapid api for data, information visualization, gym fitness exercise application.
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