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Forecasting Vehicle Prices using Machine Learning Techniques based on Federated Learning Strategy

by Mohammed A. Mahfouz, Sara M. Mosaad, Mohamed A. Belal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 20
Year of Publication: 2023
Authors: Mohammed A. Mahfouz, Sara M. Mosaad, Mohamed A. Belal
10.5120/ijca2023922923

Mohammed A. Mahfouz, Sara M. Mosaad, Mohamed A. Belal . Forecasting Vehicle Prices using Machine Learning Techniques based on Federated Learning Strategy. International Journal of Computer Applications. 185, 20 ( Jul 2023), 36-48. DOI=10.5120/ijca2023922923

@article{ 10.5120/ijca2023922923,
author = { Mohammed A. Mahfouz, Sara M. Mosaad, Mohamed A. Belal },
title = { Forecasting Vehicle Prices using Machine Learning Techniques based on Federated Learning Strategy },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2023 },
volume = { 185 },
number = { 20 },
month = { Jul },
year = { 2023 },
issn = { 0975-8887 },
pages = { 36-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number20/32811-2023922923/ },
doi = { 10.5120/ijca2023922923 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:26:36.965202+05:30
%A Mohammed A. Mahfouz
%A Sara M. Mosaad
%A Mohamed A. Belal
%T Forecasting Vehicle Prices using Machine Learning Techniques based on Federated Learning Strategy
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 20
%P 36-48
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the growth of the Internet and big data, more consumer behavior data is being used in various forecasting issues, enhancing prediction accuracy. Due to market and environmental changes, automobile sales will fluctuate as the primary mode of transportation. Accurate car sales forecasting can influence the economy and the transportation industry and enable dealers to dynamically alter their marketing strategies. A variety of factors can influence the decision to buy a vehicle, including the product's inherent characteristics, economics, policy, and other factors. Additionally, the sample data display traits from various sources, tremendous complexity, and high volatility. To estimate the monthly sales of autos, this study employs a variety of machine learning and statistical models, each of which has global optimization, a simple structure, and high generalization capabilities. Additionally, Federated Learning is perfecting the parameters to boost data security and prediction accuracy. The elements that influence auto sales are first examined and determined using statistical and machine approaches. Third, we use the dataset for the experimental analysis using the Dickey-Fuller test. The findings show that the Fed-Linear Regression model shows the best mean absolute percentage error (MAPE) and root-mean-square error (RMSE) performance. Finally, managerial implications are put forward for reference.

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Index Terms

Computer Science
Information Sciences

Keywords

Fed-KNN Fed-SVM Fed-Random Forest Fed-Neural Network Fed-Linear Regression Fed-Gradient Boosting Fed-AdaBoost.

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