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

https://doi.org/10.21744/lingcure.v5nS2.1660

Authors

  • Anu Yadav Department of computer science and engineering IGDTUW, Delhi, India
  • Ela Kumar Department of computer science and engineering IGDTUW, Delhi, India
  • Piyush Kumar Yadav Department of electrical engineering Veer Bahadur Singh Purvanchal University Jaunpur, 222003 India

Keywords:

deep learning, learning technique, machine learning, object detection, prediction system

Abstract

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.

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Published

2021-11-03

How to Cite

Yadav, A., Kumar, E., & Yadav, P. K. (2021). Object detection and used car price predicting analysis system (UCPAS) using machine learning technique. Linguistics and Culture Review, 5(S2), 1131-1147. https://doi.org/10.21744/lingcure.v5nS2.1660

Issue

Section

Research Articles