基于机器学习算法的销售预测模型的应用。

M. Abdullahi, G. Aimufua, U. Muhammad
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引用次数: 0

摘要

机器学习一直是一个在许多不同行业进行深入研究的主题,幸运的是,公司正在逐渐意识到各种机器学习方法来解决他们的问题。然而,为了充分挖掘不同机器学习模型的潜力并获得高效的结果,人们需要对模型的应用和数据的本质有很好的理解。本文旨在研究不同的方法来获得机器学习算法应用于给定的预测任务的良好结果。为此,本文批判性地分析和研究了机器学习算法在动态条件下的销售预测中的适用性,建立了基于回归模型的预测模型,并对四种机器学习回归算法(Random Forest, Extreme Gradient Boosting,使用来自尼日利亚零售商店的数据集进行销售预测,基于诸如r平方、均方根误差、平均绝对误差和平均绝对百分比误差等性能矩阵。关键词:销售预测,基于模型,算法,机器学习
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Sales Forecasting Model Based on Machine Learning Algorithms.
Machine learning has been a subject undergoing intense study across many different industries and fortunately, companies are becoming gradually more aware of the various machine learning approaches to solve their problems. However, to fully harvest the potential of different machine learning models and to achieve efficient results, one needs to have a good understanding of the application of the models and the nature of data. This paper aims to investigate different approaches to obtain good results of the machine learning algorithms applied for a given forecasting task. To this end, the paper critically analyzes and investigate the applicability of machine learning algorithm in sales forecasting under dynamic conditions, develop a forecasting model based on the regression model, and evaluate the performance of four machine learning regression algorithms (Random Forest, Extreme Gradient Boosting, Support Vector Machine for Regression and Ensemble Model) using data set from Nigeria retail shops for sales forecasting based on performance matrices such as R-squared, Root Mean Square Error, Mean Absolute Error and Mean Absolute Percentage Error. Keywords: Sales Forecasting, Model Based, Algorithms Machine Learning
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