An Exploration of Task Pricing Based on K-means Clustering and Multiple Linear Regression

This study aims to explore the task pricing law based on K-means clustering and multiple linear regression and optimize the task pricing strategy through data analysis and modeling. First, K-means clustering analysis is used to classify task data, identify the potential influencing factors of task pricing, and analyze the relationship between task pricing and each variable by combining market supply and demand theory. Based on this, a multiple linear regression model is constructed to quantify the functional relationship between task pricing and multiple factors. For the analysis of the causes of unfinished tasks, this study identifies multiple major factors. By categorizing the unfinished tasks, a targeted pricing correction model is proposed to improve the task completion rate. Meanwhile, based on the packaged distribution strategy, an optimized task release scheme is proposed to improve the efficiency and fairness of task allocation through reasonable pricing adjustment. The innovation of the study is to combine K-means clustering with multivariate linear regression, which provides a new data-driven model for task pricing and improves the task completion rate through classification correction and packing strategy. The research results can provide practical guidance for pricing optimization in similar task assignment scenarios.