Mobile Price Prediction using WEKA
Pritish Arora
, Sudhanshu Srivastava , Bindu Garg
Machine Learning, Prediction, Decision Tree, Naïve Bayes
The key purpose of this research work is to determine "If the mobile with given features would be under a certain price range." Specific feature selection algorithms are used to recognize and delete features that are less necessary and redundant, and have minimal complexity in computation.Different classifiers are used to achieve the best possible accuracy. Results are measured in terms of achieving the maximum accuracy and choosing the minimum features. Statement is made based on the algorithm for best selection of features and best classifier for the given dataset.This work can be used to find the optimal product (with minimum cost and maximum features) in any form of marketing and industry. It is suggested that future work will extend this research and find a more sophisticated solution to the given problem and a more accurate tool for estimating prices.
"Mobile Price Prediction using WEKA", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.5, Issue 4, page no.330 - 333, April-2020, Available :https://ijsdr.org/papers/IJSDR2004057.pdf
Volume 5
Issue 4,
April-2020
Pages : 330 - 333
Paper Reg. ID: IJSDR_191654
Published Paper Id: IJSDR2004057
Downloads: 000347220
Research Area: Engineering
Country: Naya Nangal, Punjab, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
