Used Cars Price Prediction using Supervised Learning Techniques
Pattabiraman Venkatasubbu1, Mukkesh Ganesh2
1Pattabiraman Venkatasubbu, Department of Computing Science and Engineering, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
2Mukkesh Ganesh, Department of Computing Science and Engineering, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
Manuscript received on 16 December 2019 | Revised Manuscript received on 23 December 2019 | Manuscript Published on 31 December 2019 | PP: 216-223 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10421291S319/19©BEIESP | DOI: 10.35940/ijeat.A1042.1291S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The production of cars has been steadily increasing in the past decade, with over 70 million passenger cars being produced in the year 2016. This has given rise to the used car market, which on its own has become a booming industry. The recent advent of online portals has facilitated the need for both the customer and the seller to be better informed about the trends and patterns that determine the value of a used car in the market. Using Machine Learning Algorithms such as Lasso Regression, Multiple Regression and Regression trees, we will try to develop a statistical model which will be able to predict the price of a used car, based on previous consumer data and a given set of features. We will also be comparing the prediction accuracy of these models to determine the optimal one.
Keywords: ANOVA, Lasso Regression, Regression Tree, Tukey’s Test.
Scope of the Article: Regression and Prediction