In order to predict Myers-Briggs personality types from text input, this research article compares the abilities of Stochastic Gradient Descent (SGD), Naive Bayes, k-Nearest Neighbours (KNN), and Logistic Regression models. The Myers-Briggs Type Indicator(MBTI) captures distinctive patterns of behaviour, cognition, and preferences of human personality. It divides people into one of sixteen personality types. This study assesses the accuracy, support, recall, precision, and F1-score for the chosen models using a dataset made up of text inputs and matching Myers-Briggs types. The project attempts to determine the most successful model among the evaluated ways for precisely predicting Myers-Briggs personality types through extensive experimentation and review. The findings of this study have applications in building accurate and reliable models for personality prediction utilizing natural language processing methods.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Machine Learning Approaches for Myers-Briggs Personality Prediction


    Beteiligte:
    Santhosh, Sethulakshmi (Autor:in) / M, Meenakshi (Autor:in) / F, Avani (Autor:in) / Abhishek, S (Autor:in) / T, Anjali (Autor:in)


    Erscheinungsdatum :

    22.11.2023


    Format / Umfang :

    813897 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch