Open AccessApplication of Sales Forecasting Model Based on Machine Learning Algorithms.
Author(s) -
Mujaheed Abdullahi,
AUTHOR_ID,
G.I.O Aimufua,
Usman Muhammad
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.22624/aims/isteams-2021/v28p17
Subject(s) - machine learning , artificial intelligence , computer science , gradient boosting , random forest , support vector machine , online machine learning , boosting (machine learning) , mean squared error , algorithm , mean absolute percentage error , ensemble learning , relevance vector machine , computational learning theory , regression , artificial neural network , unsupervised learning , mathematics , statistics
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