Forecasting the Impact of Raw Material Cost Variations on Finished Goods Pricing Using Random Forest and Naive Bayes Approach

Authors: Narendra Sharad Fadnavis

Country: USA

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Abstract: Examining the effects of raw material price fluctuations on supply chain productivity is the driving force behind this study. In particular, the study aimed to investigate how the company dealt with unpredictable raw material prices, how these variations affected the company's productivity, and what role governments played in resolving this issue.The three main parts are feature selection, model training, and preprocessing. Among the three steps that make up preprocessing—outlier preprocessing, feature smoothing, and normalization—outlier preprocessing yields the best results with the least amount of effort investment. Feature selection, often called variable or attribute selection, is a method for improving performance by reducing the number of features to a manageable number by removing superfluous or unimportant information. We used the RELM framework to train the models. Conversely, it renders ELM and CNN unnecessary. According to the numbers, 95.61 percent of the time is successful.

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Paper Id: 232357

Published On: 2023-01-05

Published In: Volume 11, Issue 1, January-February 2023

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