Machine Learning-based Analyzing Impact of Remote Work Arrangements on Employee Well-Being and Productivity

The wholly wholesale transition into remote and hybrid workspaces is characteristic of modern-day work settings. As more and more firms adopt such flexible models, it is important to note, their implications on employee wellness and productivity. This paper uses machine learning techniques in order to understand the effects of mental distancing and hybridization on various key results such as the mental state of the employees, balance between their work and private life, separation from the society, as well as productivity levels. With a sample size of 5000 employees collect from kaggle, we determine the advantages and disadvantages these work models present. In other regards, geographical larger shifts create practical benefits including greater flexibility and autonomy plus productivity. However, such dramatic shifts create higher work-related stress, greater social tension and isolation, as well as challenges with workplace culture. AI, or more precisely machine learning, uncovers concepts in relation to work models in this paper, fico that enable firms to better personate their constituents and the workforce. Reasonable conclusions could be drawn so as flexible working arrangements benefit in reducing work-life conflict however improved organizational structure within the companies is needed to have such benefits due. Mental health as one of the resources, alongside technology will assist in such transition. This paper is a step forward for HR practitioners.