Experimental Validation of Contextual Parameters and Comparative Analysis with State-of-the-Art in CARS Recommendation Systems in Ubiquitous Computing

The most important role of Consumer Behavior prediction plays in e-commerce, various ways of marketing, and Context-aware Recommendation Systems (CARS). From an Amazon consumer dataset, we conduct a comparative analysis of different machine learning models to compare their performance or effectiveness in predicting consumer behavior based on an Amazon consumer dataset. Additionally, we introduce a new algorithm combining feature selection and optimization that aims to enhance prediction accuracy. Person behavior prediction has historically helped enhance e-commerce, marketing, and Context-Aware Recommendation Systems-CARS, allowing businesses to get closer to customers and understand their needs better from the time they appeared to the time an analysis could be done. The research work performs comparative analysis among various machine learning techniques, like Logistic Regression, Decision Tree, Random Forest, SVM, and KNN, to see which one is more effective in predicting customer behavior, based on an Amazon consumer dataset. Besides, a new algorithm that merges feature selection and optimization is proposed and implemented to guarantee better prediction accuracy. The project is aimed at the creation of data-driven decision systems powered by an optimized machine learning framework for customer analytics.