Exploratory Data Analysis using Machine learning – Behaviour Based Safety

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Jerin Johnkutty, Serajul Haque, Jerry Davis T


This study takes a gander at the usage of machine learning techniques for exploratory data examination (EDA) in the field of behavior-based safety (BBS), with a particular focus on the examination of safety dimension datasets got from worker reviews drove in industrial settings. We utilize a methodology that integrates an extent of visualization methods, statistical examinations, and parameter evaluations to uncover complex encounters into safety perceptions and ways of behaving. We do this by utilizing the Python programming language and its strong data investigation libraries, including MATplotlib, Seaborn, and Pandas. Our survey means to assist proof based decision-making strategies, proactively distinguish potential for developing safety protocols, and develop a organizational culture that is safety-centric driven by eagerly examining worker feedback and behavioral patterns. This research features the significant role of EDA and machine learning in deciphering complex datasets, advancing substantial improvements in occupational safety, and putting a high priority on workers' well-being in dynamic workplaces through synergistic cooperation between data analytics and domain expertise.   

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