Stress Level Detection Uisng ML

Keywords: Stress level detection,Machine learning,prediction

Background

In today's fast-paced society, stress has emerged as a widespread issue that contributes to a range of physical and mental health problems. Therefore, it is crucial to identify and manage stress levels in individuals to promote overall well-being and prevent long-term health complications. This paper introduces a stress level detection system that utilizes machine learning (ML) techniques to analyse physiological and behavioural data. The proposed system employs wearable devices, such as heart rate monitors, electro dermal activity sensors, and accelerometers, to capture real-time physiological signals and activity patterns. These devices gather data related to indicators of stress, including heart rate variability, skin conductance, and physical movements

Aim & Objectives

Aim
The main aim of this project is to Develop ML-based system to detect and classify stress levels for effective monitoring and management.
Objective
• Data cleaning and pre-processing
• Data visualization
• Checking for outliers
• Build the ML model

Research Methodology

various classification algorithms like logistic regression,KNN,Rf,DT and XGBoost, ADABoost

Software & Tools

Jupyter, Google Collab

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