With remarkable technological advancements and the emergence of new technical expertise, combined with substantial economic growth within our nation, there has been a noticeable shift in consumer preferences towards the purchase of automobiles, particularly cars, over other modes of transportation. This research paper aims to conduct a comprehensive analysis of the prevailing trends in used car pricing. Additionally, it endeavors to predict the prices of used cars using supervised machine learning algorithms. Considerable number of distinct attributes are examined for the reliable and accurate prediction. Also, it seeks to identify the most effective machine learning algorithm among those selected for the purpose of predicting car prices. Previous studies have already explored machine learning algorithms such as linear regression, multiple regression, and random forest in this context. Our objective is to assess which algorithm offers the most reliable and accurate predictions for car prices. This research has practical implications, as it can benefit first-time used car buyers by providing them with valuable insights into pricing dynamics. Furthermore, the model was evaluated using test data and the accuracy of 91% was obtained.


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    Titel :

    Automobile Valuation Prediction Using Machine Learning based Algorithms


    Beteiligte:
    Deepak, N A (Autor:in) / Kumar, Ravi (Autor:in) / Gupta, Tarun (Autor:in) / Gaurav, Shubham (Autor:in) / Yadav, Pulkit Singh (Autor:in) / Pranesh, B (Autor:in)


    Erscheinungsdatum :

    28.11.2023


    Format / Umfang :

    566542 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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