Unlocking Mental Wellness through Yoga Analytics: A Catalyst for Implementing NEP-2020's Vision

Yoga and meditation are increasingly recognized as effective practices for enhancing holistic wellness, particularly in reducing stress, improving focus, and promoting mental clarity. This study utilizes a survey-based dataset which includes demographic details, yoga and meditation habits, perceived benefits, work-life patterns, screen time, stress levels, and opinions on meditation’s role in stress management. The main objective is to explore associations between lifestyle variables and wellness perceptions while applying statistical and machine learning techniques to develop deeper insights. Exploratory data analysis, correlation measures, and hypothesis testing were employed to assess the relationship between yoga practice and stress reduction. A statistical result demonstrates that individuals practicing yoga or meditation on a daily basis presented stronger belief in its necessity and higher awareness of its benefits. Additionally, longer screen time (greater than eight hours) was associated with increased stress, while positive attitudes toward meditation were strongly linked to perceived stress relief. To complement these findings, machine learning approaches were implemented. Classification models such as Logistic Regression, Decision Trees, Random Forest, and Support Vector Machine were applied to predict stress outcomes using predictors such as yoga frequency, working hours, and perceptions of meditation. The predictive modelling estimated the probability of stress relief when meditation was practiced regularly under varying work conditions. Lastly, this integrated Statistical and Machine Learning framework demonstrates the utility of data science in extracting actionable wellness insights from yoga and health datasets, thereby supporting the design of personalized preventive healthcare strategies.