Stress Classification Using Gaussian Mixture Models (Bayesian Approach)

This repository contains research on classifying stress levels using Gaussian Mixture Models (GMMs) with a Bayesian approach.
The methodology leverages machine learning and statistical techniques, including the Expectation-Maximization (EM) algorithm, for estimating parameters of Gaussian mixtures in classification tasks.

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📊 Dataset

The project uses the Nurse Stress Prediction: Wearable Sensors Dataset, available on Kaggle:

• Nurse Stress Prediction Dataset

This dataset includes physiological and contextual data collected from wearable sensors for monitoring stress levels in real-world settings.

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📚 References

The following references provide background on Gaussian Mixture Models, clustering, and probabilistic approaches to machine learning:

1. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

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2. McLachlan, G. J., & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience.

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3. Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.

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4. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer.

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5. Reynolds, D. A. (2009). Gaussian Mixture Models. In Encyclopedia of Biometrics (pp. 659–663). Springer US.

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6. McLachlan, G. J., & Basford, K. E. (1988). Mixture Models: Inference and Applications to Clustering. Marcel Dekker.

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📑 Report

For detailed methodology, results, and analysis, please refer to the full report available in this repository.