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Academic Journal of Computing & Information Science, 2024, 7(2); doi: 10.25236/AJCIS.2024.070215.

Logistics Cost Prediction Based on Random Forest Model


Feifei Xie1, Wei Yu2

Corresponding Author:
Feifei Xie

1School of Business, Anhui University, Hefei, 230601, China

2School of Automotive and Transportation Engineering, Jiangsu University, Zhenjiang, 212013, China


Predicting logistics costs is imperative for enterprises to effectively manage and make informed decisions regarding logistics expenditures. This paper employs a systematic approach to this end. Initially, a Pearson correlation analysis is conducted on variables including Line Item Quantity, Line Item Value, Weight, and Freight Cost, revealing a strong correlation between Weight and Freight Cost. Subsequently, a chi-square test is applied to variables such as Fulfill Via, Vendor INCO Term, Shipment Mode, Product Group, Sub Classification, and Freight Cost. This analysis identifies Vendor INCO Term, Shipment Mode, and Sub Classification as pivotal influencing factors. To further enhance predictive accuracy, K Nearest Neighbour Regression (KNN), Support Vector Regression (SVR), and Random Forest (RF) models are individually employed for logistics cost prediction. Comparative analysis of prediction errors indicates that the RF model outperforms KNN, SVR, and other models, showcasing superior logistics cost prediction accuracy.


Logistics Costs, Random Forest, Chi-square Test, Support Vactor Regression, K-Nearest Neighbor

Cite This Paper

Feifei Xie, Wei Yu. Logistics Cost Prediction Based on Random Forest Model. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 2: 109-114. https://doi.org/10.25236/AJCIS.2024.070215.


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