Predicting True Value of Used Car using Multiple Linear Regression Model
Laveena D’Costa1, Ashoka Wilson D’Souza2, Abhijith K3, Deepthi Maria Varghese4
1Laveena D’Costa, Department of Big Data Analytics, AIMIT, St. Aloysius College, Mangalore (Karnataka), India.
2Ashoka Wilson D’Souza, Department of Statistics, Mangalore University, Mangalore (Karnataka), India.
3Abhijith K, Department of Big Data Analytics, AIMIT, St. Aloysius College, Mangalore (Karnataka), India.
4Deepthi Maria Varghese, Department of Big Data Analytics, AIMIT, St. Aloysius College, Mangalore (Karnataka), India.
Manuscript received on 13 February 2020 | Revised Manuscript received on 20 February 2020 | Manuscript Published on 28 February 2020 | PP: 42-45 | Volume-8 Issue-5S February 2020 | Retrieval Number: E10100285S20/2020©BEIESP | DOI: 10.35940/ijrte.E1010.0285S20
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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: Predicting the true value of used cars requires lot of analysis. This prediction takes into account variables such as car model, fuel type, number of owner and so on. In this paper we are applying machine learning algorithms to determine the true value of cars when selling them to the dealers. We have used multiple linear regression model by dividing the data into training and test. Vehicle price forecast is both a critical and significant job, particularly when the car is used and does not come directly from the factory.
Keywords: Multiple Linear Regression, True Value.
Scope of the Article: Artificial Intelligent Methods, Models, Techniques