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Object detection and used car price predicting analysis system (Ucpas) using machine learning technique

Bibliographic Data

ID22250036
AuthorsAnu Yadav (0009-0006-3240-9945, Indira Gandhi Delhi Technical University for Women, corresponding author), Ela Kumar (0000-0001-5798-6972, Indira Gandhi Delhi Technical University for Women, corresponding author), Piyush Kumar Yadav (Veer Bahadur Singh Purvanchal University, corresponding author)
Year2021
Volume5
IssueS2
Pages1131-1147
Publication date2021-11-03
Open AccessYes
TypeARTICLE
VenueLinguistics and Culture Review (JOURNAL)
Journal identifiersISSN: 2690-103X • E-ISSN: 2690-103X
PublisherScientific and Literature Open Access Publishing (PUBLISHER)
DOI10.21744/lingcure.v5ns2.1660
OpenAlexW3212421821
LanguageEN
References cited10

The highly interesting research area that noticed in the last few years is object detection and find out the prediction based on the features that can be benefited to consumers and the industry. In this paper, we understand the concept of object detection like the car detection, to look into the price of a second-hand car using automatic machine learning methods. We also understand the concept of object detection categories. Nowadays, the most challenging task is to determine what is the listed price of a used car on the market, Possibility of various factors that can drive a used car price. The main objective of this paper is to develop machine learning models which make it possible to accurately predict the price of a second-hand car according to its parameter or characteristics. In this paper, implementation techniques and evaluation methods are used on a Car dataset consisting of the selling prices of various models of car across different cities of India. The outcome of this experiment shows that clustering with linear regression and Random Forest model yield the best accuracy outcome. The machine learning model produces a satisfactory result within a short duration of time compared to the aforementioned self

Cluster analysis · Machine learning · Object detection · Random forest · Support vector machine · Computer Science · Energy, Environment, and Transportation Policies · Engineering · Mathematics · Artificial Intelligence

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