Machine Learning Based Predictive Analysis of Tariff Rate Prediction

A tariff rate is a price at which a certain cargo is delivered from one point to another. The price depends on the form of the cargo, the mode of transport, the weight of the cargo, and the distance to the delivery destination. Simple mean applied tariff is the unweight average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Less-than-truckload (LTL) is used for smaller truck freight loads. In single trucks LTL carriers hold several shipments for numerous customers. Combining several cargo owner’s cargoes in one truck reduces the cost each cargo owner must pay. Shipping by LTL offers significant savings over shipping the same load in a dedicated truck. The details were listed at six- or eight-digit point utilizing the Harmonized Trading Framework. Tariff line results have been combined with Standard International Trade Classification (SITC) revision 3 codes to identify product groupings. Specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs.to take the truck traveling details and it traveling cost for predict that truck overall traveling cost with tariff rate. We can collect the details from real time field. Applying regression method for prediction, here we apply linear regression, support vector regression, random forest regression, and decision tree regression. Using this all algorithm to find best one for prediction.

Keywords: Tariff, Classification, Prediction, Algorithm, Regression, Machine Learning.

Suggested Citation: Suggested Citation

V, Arul Kumar and S, Agalya and S, Gokul Chakkaravarthi and E, Gokula Priya, Machine Learning Based Predictive Analysis of Tariff Rate Prediction (March 31, 2020). IJETIE VOL. 6, ISSUE 3, MARCH 2020, Available at SSRN: https://ssrn.com/abstract=3564907