Holistic wellness tracking with stress detection using machine learning

Abstract

In today's fast-paced world, maintaining holistic wellness is paramount for individuals to lead fulfilling lives. This project focuses on leveraging data analytics techniques, including machine learning algorithms such as Bernoulli Naive Bayes, to develop a holistic wellness tracking system with stress detection capabilities. The project utilizes various Python libraries such as Pandas, NumPy, Matplotlib, NLTK, and scikit-learn to analyze, clean, manipulate data, and derive actionable insights. Data related to various wellness parameters such as physical activity, sleep patterns, dietary habits, and emotional states are collected from wearable devices, mobile apps, and surveys. Pandas is used for data ingestion, cleaning, and preprocessing. Numpy facilitates handling and processing of multidimensional arrays, especially for mathematical operations. Data preprocessing includes handling missing values, standardizing features, and encoding categorical variables. Relevant features related to wellness and stress indicators are identified and extracted from the raw data. Natural Language Processing (NLP) techniques from NLTK are applied to process textual data, such as sentiments from social media posts or journal entries related to daily experiences. Count Vectorizer from scikit-learn is utilized to transform textual data into numerical vectors based on word frequencies, enabling incorporation of textual features into the machine learning models. Bernoulli Naive Bayes classifier is employed for stress detection based on features extracted from wellness data. Other machine learning algorithms from scikit-learn can also be explored for classification tasks, depending on the dataset characteristics and performance metrics. The performance of the stress detection model is evaluated using metrics such as accuracy, precision, recall, and F1-score. Cross-validation techniques are employed to ensure robustness and generalization of the model. Matplotlib is utilized to create static, animated, and interactive visualizations to present insights derived from the data. Visualizations may include trends in wellness parameters over time, correlations between different wellness factors, and stress levels in response to various stimuli.