An Approach for Predicting the Costs of Forwarding Contracts using Gradient Boosting

Abstract

Predicting the cost of forwarding contract is a severe challenge to road transport management system. The transportation cost of a forwarding contract often depends on many factors. It is hard for humans to evaluate the various factors in transportation and calculate the cost of forwarding contract. In this paper, we propose an approach to address such a problem by following the sequence of machine learning steps which consist of data analysis, feature engineering and model construction. First, we conduct a detailed analysis of the given data. Then, we generate effective features to characterize the cost of forwarding contract and eliminate redundant features. Finally, in the model construction phase, we propose a gradient boosting decision tree based method to train and predict the cost of forwarding contract. The proposed approach achieves RMSE scores of 0.1391 on the test set, which is the 2nd final score in the competition.