https://github.com/adadalshabab/human-stress-analysis-greadsearch-classifier

The project leverages data from physiological signals, self-reported surveys, behavioral observations, or other relevant sources to infer and analyze stress levels.
https://github.com/adadalshabab/human-stress-analysis-greadsearch-classifier

classification knn-classification machine-learning machine-learning-algorithms matplotlib pandas scikit-learn

Last synced: 16 days ago
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The project leverages data from physiological signals, self-reported surveys, behavioral observations, or other relevant sources to infer and analyze stress levels.

• Host: GitHub
• URL: https://github.com/adadalshabab/human-stress-analysis-greadsearch-classifier
• Owner: AdadAlShabab
• Created: 2024-04-09T13:12:56.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-04-09T13:15:34.000Z (10 months ago)
• Last Synced: 2024-11-20T21:56:39.758Z (3 months ago)
• Topics: classification, knn-classification, machine-learning, machine-learning-algorithms, matplotlib, pandas, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 171 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Human Stress Analysis

This repository houses a project focused on human stress analysis, aiming to understand and quantify stress levels in individuals using various data sources and analytical techniques. The project leverages data from physiological signals, self-reported surveys, behavioral observations, or other relevant sources to infer and analyze stress levels.

## Dataset

The dataset used in this project may consist of various types of data, including physiological signals such as heart rate variability (HRV), electrodermal activity (EDA), accelerometer data, self-reported surveys measuring stress levels, and contextual data such as time of day, activity level, or environmental factors. This multi-modal dataset allows for a comprehensive analysis of stress across different dimensions.

## Project Structure

The project structure is organized as follows:

- **data:** Contains the dataset used for stress analysis.

- **notebooks:** Includes Jupyter notebooks detailing the data preprocessing steps, feature extraction, modeling, and evaluation.

- **src:** Contains the Python code for any custom data processing, feature engineering, or modeling techniques used in the project.

- **reports:** Contains reports generated from the stress analysis, including visualizations, summary statistics, and insights.

## Data Preprocessing

Data preprocessing involves cleaning, filtering, and transforming raw data into a format suitable for analysis. For physiological signals, this may include noise removal, artifact detection, and feature extraction. Self-reported survey data may require encoding categorical variables and handling missing values. Contextual data may need to be normalized or standardized for consistency.

## Feature Extraction

Feature extraction involves deriving relevant features from the raw data that capture different aspects of stress. For physiological signals, features such as heart rate, skin conductance level, and activity level may be extracted. Self-reported survey data may yield features related to perceived stress levels, coping strategies, or symptom severity. Contextual features may include time-based features, location data, or social context.

## Modeling and Analysis

The stress analysis task involves building predictive models to estimate stress levels based on the extracted features. Various machine learning algorithms such as regression, classification, or clustering may be employed, depending on the nature of the analysis. Model evaluation is performed using appropriate metrics such as accuracy, precision, recall, F1-score, or correlation coefficients.

## Getting Started

To get started with this project, follow these steps:

1. Clone this repository to your local machine.

2. Navigate to the directory to explore the Jupyter notebooks containing the analysis and modeling process.

3. Ensure you have the necessary dependencies installed. You can install them via pip using the `requirements.txt` file.

```bash

pip install -r requirements.txt

```

4. Run the notebooks sequentially to replicate the analysis and understand the steps involved.

## Requirements

The project requires Python 3.11 along with various libraries such as pandas, numpy, matplotlib, seaborn, scikit-learn, etc. These dependencies are listed in the `requirements.txt` file.

## Contributing

Contributions to this project are welcome. If you find any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request.

Feel free to customize this README according to your project's specifics and preferences.