A Study on Product Evaluation Models Based on Correlation Analysis and Linear Fitting

Abstract

In this study, we commence by thoroughly preprocessing the data, a crucial step that involves cleansing, normalizing, and structuring the dataset to ensure its integrity and readiness for in-depth analysis. This meticulous preprocessing phase is essential to eliminate noise and inconsistencies, thereby enhancing the accuracy and reliability of the subsequent analytical processes. Following the preprocessing, we engage in a comprehensive analysis aimed at uncovering the intricate relationships within the data. Through this analysis, we successfully derive a sophisticated relational model that interlinks Star Ratings, Reviews, and Helpfulness Ratings. This model provides a nuanced understanding of how these elements interact, offering valuable insights into consumer behavior and preferences. Leveraging the insights gained from the relational models, we proceed to develop a robust framework for product evaluation. This framework is designed to be both flexible and scalable, allowing for its application across various product categories and user groups. It integrates multiple dimensions of consumer feedback, enabling a more holistic assessment of product quality and user satisfaction. The framework's effectiveness is further validated through rigorous testing, demonstrating its potential to serve as a reliable tool for businesses seeking to optimize their product offerings and enhance customer engagement.