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Survey on Pre-Owned Car Price Prediction Using Random Forest Algorithm

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ICT for Intelligent Systems ( ICTIS 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 361))

Abstract

The technique of determining an or used new value of the car is known as car price prediction. The make, type, age, miles, quality, as well as characteristics of a car are just a few of the many elements that go into determining its worth. An investigation of consumer perceptions regarding the importance of cars is called a car sale forecast survey. In this review, we intend to study various machine learning techniques, including Random Forest, Linear Regression and Support Vector Machine. On automobile datasets, experiments can be performed to assess effectiveness using parameter estimates.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, M.Kumarasamy College of Engineering, Thalavapalayam, Karur, Tamil Nadu, India

    C. Selvarathi, G. Bhava Dharani & R. Pavithra

Authors
  1. C. Selvarathi
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  2. G. Bhava Dharani
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  3. R. Pavithra
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Corresponding author

Correspondence to C. Selvarathi .

Editor information

Editors and Affiliations

  1. Hertfordshire Business School, University of Hertfordshire, Hatfield, Hertfordshire, UK

    Jyoti Choudrie

  2. Department of Artificial Intelligence and Data Science, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India

    Parikshit N. Mahalle

  3. University Putra Malaysia, Serdang, Selangor, Malaysia

    Thinagaran Perumal

  4. Global Knowledge Research Foundation, Ahmedabad, Gujarat, India

    Amit Joshi

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Selvarathi, C., Bhava Dharani, G., Pavithra, R. (2023). Survey on Pre-Owned Car Price Prediction Using Random Forest Algorithm. In: Choudrie, J., Mahalle, P.N., Perumal, T., Joshi, A. (eds) ICT for Intelligent Systems. ICTIS 2023. Smart Innovation, Systems and Technologies, vol 361. Springer, Singapore. https://doi.org/10.1007/978-981-99-3982-4_15

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