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This paper presents a robust machine learning pipeline designed to classify individuals as introvert or extrovert using engineered behavioral and social interaction metrics. A dataset of 2,900 instances with eight behavioral attributes supports the engineering of three composite features, validated via ablation studies and algorithm benchmarking.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/computational-personality-assessment-a-data-driven-machine-learning-approach-using-engineered-behavioral-features/293160/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/computational-personality-assessment-a-data-driven-machine-learning-approach-using-engineered-behavioral-features/293160.png","ImageObject",300,407,{"name":92,"@type":93},"Marry","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What personality traits does the proposed model classify?","Question",{"text":112,"@type":113},"The model classifies individuals as introvert or extrovert based on engineered behavioral and social interaction metrics.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What engineered features are introduced in the study?",{"text":117,"@type":113},"The study engineers three composite features: social ratio, social engagement, and recovery need to capture latent psychological constructs.",{"name":119,"@type":110,"acceptedAnswer":120},"How is model performance evaluated and compared?",{"text":121,"@type":113},"The framework evaluates 13 learning algorithms and uses ablation studies to quantify how engineered features improve accuracy versus using only original features.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},293160,1789660494,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},5909892079670,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Computational Personality Assessment: A DataDriven Machine Learning Approach Using Engineered Behavioral Features  \nMEERA SOLGAMA  \nAhmedabad University  \nZEELKUMAR BHAVSAR  \nIntas Pharmaceuticals (India)  \nResearch Article  \nKeywords: Personality Classification, Machine Learning, Feature Engineering, K-Nearest Neigh-bors, Behavioral Analytics, Ablation Study  \nPosted Date: July 30th, 2026  \nDOI: [https://doi.org/10.21203/rs.3.rs-10325295/v1](https://doi.org/10.21203/rs.3.rs-10325295/v1)  \nLicense: 􀁱 􀅏 This work is licensed under a Creative Commons Attribution 4.0 International License.  \nRead Full License  \nAdditional Declarations: No competing interests reported.  \nComputational Personality Assessment: A Data-Driven Machine Learning Approach Using Engineered Behavioral  \nFeatures  \nMeera J. Solgama 1,* ORCID,  \nZeelkumar A. Bhavsar2 ORCID  \n1Department of Computer Engineering, Ahmedabad University, Ahmedabad, India  \n2Department of Organic Chemistry, Intas Pharmaceuticals, Ahmedabad, India  \n*Corresponding author: Meera J. Solgama,  \n[e-mail: meera.s@ahduni.edu.in](e-mail: meera.s@ahduni.edu.in)  \nAbstract  \nAutomated personality assessment has emerged as a critical research frontier at the intersection of computational psychology and machine learning, with applications ranging from human resources and adaptive systems to mental health screening and forensic analysis. This paper presents a robust machine learning pipeline designed to classify individuals as introvert or extrovert using engineered behavioral and social interaction metrics. We draw upon a dataset comprising 2,900 instances, each characterized by eight behavioral attributes, including social engagement frequency, recovery patterns, and digital activity indicators. Our methodological contribution lies in the systematic engineering of three composite features—social ratio, social engagement, and recovery need—designed to capture latent psychological constructs not directly observable in raw questionnaire responses. Through comprehensive ablation studies, we quantitatively demonstrate the contribution of these engineered features, showing that they provide a 12.4% improvement in classification accuracy compared to using only original features. Our experimental framework rigorously evaluates 13 distinct learning algorithms, ranging from linear and ensemble models to neural networks and probabilistic classifiers. The best performing model, K-Nearest Neighbors, attains an accuracy of 91.90%, while RBF Support Vector Machine and Gaussian Naive Bayes follow closely at 91.72% . We perform in-depth analysis of why K-NN performs best, attributing its success to the well-clustered nature of the engineered feature space. Paired t-tests confirm that differences among top models are not statistically significant (p > 0.05), providing practitioners flexibility in model selection. Feature importance analysis ranks the engineered features among the most influential predictors, validating our feature engineering hypothesis. This study contributes validated, theoretically-grounded models to computational psychology, with immediate practical implications and clear pathways for future research.  \nKeywords: Personality Classification 􀂷 Machine Learning 􀂷 Feature Engineering 􀂷 K-Nearest Neighbors 􀂷 Behavioral Analytics 􀂷 Ablation Study  \nStatements and Declarations  \nCompeting Interests: The authors have no relevant financial or non-financial interests to disclose. Funding: The authors did not receive support from any organization for the submitted work.  \nData Availability: The dataset used in this study is publicly available at: [https://www.kaggle.com/](https://www.kaggle.com/)[ ](https://www.kaggle.com/)[datasets/rakeshkapilavai/extrovert-vs-introvert-behavior-data](datasets/rakeshkapilavai/extrovert-vs-introvert-behavior-data. The code used for analysis is)[. The code used for analysis is](datasets/rakeshkapilavai/extrovert-vs-introvert-behavior-data. The code used for analysis is","cbCaic4WOea3uPGq","https://ap.wps.com/l/cbCaic4WOea3uPGq","pdf",682612,17,"English","# Abstract\n# Keywords\n# Statements and Declarations\n# Data Availability\n# Author Contributions\n# 1 Introduction","[{\"question\":\"What personality traits does the proposed model classify?\",\"answer\":\"The model classifies individuals as introvert or extrovert based on engineered behavioral and social interaction metrics.\"},{\"question\":\"What engineered features are introduced in the study?\",\"answer\":\"The study engineers three composite features: social ratio, social engagement, and recovery need to capture latent psychological constructs.\"},{\"question\":\"How is model performance evaluated and compared?\",\"answer\":\"The framework evaluates 13 learning algorithms and uses ablation studies to quantify how engineered features improve accuracy versus using only original features.\"}]","Computational Personality Assessment - A Data-Driven Machine Learning Approach Using Engineered Behavioral Features | PDF",43]