Road accidents significantly challenge developing countries, mostly due to inadequate traffic control management and population density. According to current research, it is projected that by the year 2030, road accidents will become the 5th top cause of fatalities worldwide. The underlying cause of this significant issue remains uncertain because of the involvement of various intricate aspects, such as climatic circumstances, road and traffic conditions, violations of road safety regulations, and the driver's personal mental and health-related concerns. This research article aims to ascertain the primary determinants that contribute to traffic accidents, specifically focusing on driver behavior, the nature of the event, and socioeconomic variables. The study aimed to discover the primary features influenced by the decision tree approach (DT) in order to determine the most likely causes based on the principal target variable, which is the classification of accidents. The qualities were examined through the application of a machine learning technique known as decision tree (DT) using the Python programming language.


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    Titel :

    Road Accident Analysis Using Machine Learning Algorithm


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Kumar, Sandeep (Herausgeber:in) / Hiranwal, Saroj (Herausgeber:in) / Garg, Ritu (Herausgeber:in) / Purohit, S.D. (Herausgeber:in) / Verma, Manisha (Autor:in) / Sharma, Bharti (Autor:in)

    Kongress:

    International Conference on Communication and Computational Technologies ; 2024 ; Jaipur, India January 07, 2024 - January 08, 2024



    Erscheinungsdatum :

    19.01.2025


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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