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@article{164249,
        author = {O.Durga Bhavani and P.Lalitha and K.Mahalakshmi and G.Praneeth Samuel and G.Sindhu},
        title = {Holistic Wellness Tracking with Stress Detection using Machine  learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {349-352},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164249},
        abstract = {Stress is a prevalent mental health issue affecting individuals worldwide, leading to various physical and psychological health problems. Early detection and intervention are crucial in managing stress effectively. Machine learning (ML) techniques offer promising approaches for detecting stress based on various data sources, including physiological signals, behavioral patterns, and textual data.
The Stress Detection project leverages Python libraries such as Pandas, NumPy, Matplotlib, NLTK (Natural Language Toolkit) and machine learning algorithms like Naïve Bayes to detect stress in human beings. By employing count vectorizer we can convert a collection document into a matrix format. Converting text data into numerical format can be used as input to the machine learning algorithms (Naïve Bayes, etc).
},
        keywords = {},
        month = {},
        }