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Reseach Article

Mobile Price Class prediction using Machine Learning Techniques

by Muhammad Asim, Zafar Khan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 29
Year of Publication: 2018
Authors: Muhammad Asim, Zafar Khan
10.5120/ijca2018916555

Muhammad Asim, Zafar Khan . Mobile Price Class prediction using Machine Learning Techniques. International Journal of Computer Applications. 179, 29 ( Mar 2018), 6-11. DOI=10.5120/ijca2018916555

@article{ 10.5120/ijca2018916555,
author = { Muhammad Asim, Zafar Khan },
title = { Mobile Price Class prediction using Machine Learning Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2018 },
volume = { 179 },
number = { 29 },
month = { Mar },
year = { 2018 },
issn = { 0975-8887 },
pages = { 6-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number29/29158-2018916555/ },
doi = { 10.5120/ijca2018916555 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:56:51.858557+05:30
%A Muhammad Asim
%A Zafar Khan
%T Mobile Price Class prediction using Machine Learning Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 29
%P 6-11
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

To predict “If the mobile with given features will be Economical or Expensive” is the main motive of this research work. Real Dataset is collected from website www.GSMArena.com . Different feature selection algorithms are used to identify and remove less important and redundant features and have minimum computational complexity. Different classifiers are used to achieve as higher accuracy as possible. Results are compared in terms of highest accuracy achieved and minimum features selected. Conclusion is made on the base of best feature selection algorithm and best classifier for the given dataset. This work can be used in any type of marketing and business to find optimal product(with minimum cost and maximum features). Future work is suggested to extend this research and find more sophisticated solution to the given problem and more accurate tool for price estimation.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Machine Learning Prediction Decision Tree Naïve Bayes