PERSONALITY PREDICTION SYSTEM

PERSONALITY PREDICTION SYSTEM

Sakshi Shenavi, Harshada Patil, Akanksha Patil, Amruta Sathe, Akshata Chougale, Siddhi Prabhavalkar, Prof. P. R. Patil

Department of Computer Science and Engineering, D. Y. Patil College of Engineering and Technology, Kolhapur, Maharashtra.

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Abstract - Personality prediction is a challenging yet crucial task in various fields such as psychology, human resources, and marketing. In this study, we propose a questionnaire-based approach using the random forest algorithm to predict personality traits. The questionnaire is designed to gather information related to the personality traits: openness, conscientiousness, extraversion, agreeableness, and neuroticism.

The dataset used for this study consists of responses from individuals who completed the questionnaire. Responses to specific questions are used as input variables for the random forest algorithm. The algorithm is trained on a portion of the dataset and then tested on the remaining portion to evaluate its performance in predicting personality traits.

Our results show that the random forest algorithm achieves high accuracy in predicting personality traits, outperforming other machine learning algorithms such as logistic regression and support vector machines. This approach has the potential to be used in various applications, such as personalized marketing, recommendation systems, and mental health assessment.

Key Words:  Personality prediction, Random forest algorithm, personality traits, Questionnaire-based approach.