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The study of personality traits through handwriting analysis has garnered significant interest due to its potential applications in psychology, hiring, and personal growth. This research investigates the feasibility of predicting specific personality traits—agreeableness, extroversion, conscientiousness, openness, and neuroticism—through handwriting patterns. The project involves data collection of diverse handwriting samples with corresponding personality trait labels, followed by preprocessing, feature extraction, model training, and validation. Key steps include standardizing handwriting samples, extracting relevant features like stroke patterns and spacing, and utilizing machine learning techniques for model training. The dataset sourced from Kaggle provides a robust foundation, which is cleansed and standardized for analysis. Our hypothesis posits that handwriting patterns encode personality traits, and by leveraging machine learning, we can accurately predict these traits. Python libraries such as sklearn, Pandas, Tensor Flow, and Open CV are employed for analysis and visualization. The research aims to contribute to the burgeoning field of handwriting analysis and provide a reliable method for predicting personality traits with practical implications in various domains.