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Reseach Article

Vehicle Price Prediction System using Machine Learning Techniques

by Kanwal Noor, Sadaqat Jan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 167 - Number 9
Year of Publication: 2017
Authors: Kanwal Noor, Sadaqat Jan
10.5120/ijca2017914373

Kanwal Noor, Sadaqat Jan . Vehicle Price Prediction System using Machine Learning Techniques. International Journal of Computer Applications. 167, 9 ( Jun 2017), 27-31. DOI=10.5120/ijca2017914373

@article{ 10.5120/ijca2017914373,
author = { Kanwal Noor, Sadaqat Jan },
title = { Vehicle Price Prediction System using Machine Learning Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2017 },
volume = { 167 },
number = { 9 },
month = { Jun },
year = { 2017 },
issn = { 0975-8887 },
pages = { 27-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume167/number9/27802-2017914373/ },
doi = { 10.5120/ijca2017914373 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:14:54.798996+05:30
%A Kanwal Noor
%A Sadaqat Jan
%T Vehicle Price Prediction System using Machine Learning Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 167
%N 9
%P 27-31
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a vehicle price prediction system by using the supervised machine learning technique. The research uses multiple linear regression as the machine learning prediction method which offered 98% prediction precision. Using multiple linear regression, there are multiple independent variables but one and only one dependent variable whose actual and predicted values are compared to find precision of results. This paper proposes a system where price is dependent variable which is predicted, and this price is derived from factors like vehicle’s model, make, city, version, color, mileage, alloy rims and power steering.

References
  1. Pudaruth,S. 2014. “Predicting the Price of Used Cars Using Machine Learning Techniques”, International Journal of information & Computation Technology,4(7), p.753-764.
  2. Kuiper, S. 2008. “Introduction to Multiple Regression: How Much Is Your Car Worth?”, Journal of Statistics Education, 16(3).
  3. Listiani M. 2009. Support Vector Regression Analysis for Price Prediction in a Car Leasing Application. Master Thesis. Hamburg University of Technology.
  4. Limsombunchai, V. 2004. House price prediction: Hedonic price model vs. artificial neural network. In New Zealand Agricultural and Resource Economics Society Conference, New Zealand, pp. 25-26.
  5. Bourassa, S.C., Cantoni, E. and Hoesli, M. 2007. “Spatial dependence, housing submarkets, and house price prediction”, The Journal of Real Estate Finance and Economics, 35(2), p.143-160.
  6. Nau, R. 2014. Notes on linear regression analysis, Lecture handouts, Duke University, Furqa School of Business, 26 nov 2014.
  7. Singh, Y., Bhatia, P. K., & Sangwan, O. 2007. “A review of studies on machine learning techniques”, International Journal of Computer Science and Security, 1(1), 70-84.
  8. Frost, J. 2013. Regression analysis: How do I interpret R- squared and assess the goodness-of-fit. The Minitab Blog, 30. Available online from: http://blog.minitab.com/blog/adventures-in-statistics/regression-analysis-how-do-i-interpret-r-squared-and-assess-the-goodness-of-fit (Last accesed: 29-11-206).
  9. Frost,J. 2013. Multiple Regression Analysis: Use Adjusted R-squared and Predicted R-squared to Include the Correct Number of Variables. Available online from:http://blog.minitab.com/blog/adventures-in-statistics/multiple-regession-analysis-use-adjusted-r-squared-and-predicted-r-squared-to-include-the-correct-number-of-variables (Last accessed: 29-11-2016).
  10. Minitab Express Support. Interpret all statistics and graphs for Multiple Regression.[Online] Available from: http://support.minitab.com/en-us/minitab-express/ 1/help-and-how-to/modeling-statistics/regression/how-to/multiple-regression/interpret-the-results/all-statistics-and-graphs/
  11. Minitab Express Support. Interpret all statistics for Predict.[Online] Available from: http://support.minitab .com/en-us/minitab-express/1/help-and-how-to/modeling -statistics/regression/how-to/predict/interpret-the-results/ all-statistics/
  12. Frost,J. 2012. How to Predict with Minitab:Using BMI to Predict the Body Fat Percentage,Part 2.[Online] Feb 23 2012. Available from: http://blog.minitab.com/blog/ adventures-in-statistics/how-to-predict-with-minitab-using-bmi-to-predict-the-body-fat-percentage-part-2
  13. https://www.pakwheels.com/ (Last accessed on 29-11- 2016)
Index Terms

Computer Science
Information Sciences

Keywords

Multiple Linear regression Car Price Regression model.