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Data Mining and Machine Learning @ CEID

Technologies

NumPy, Matplotlib, Pandas, scikit-learn, Pytorch, pgmpy, SciPy, Seaborn

Dataset

Human Activity Recognition Trondheim (HARTH) The HARTH dataset, contains data collected from two accelerometers worn by 22 participants in a study. The data collection lasted approximately 2 hours in a free-movement environment.
Performed an initial assessment of the data, followed by classification based on activity using

  • Neural Network implemented in PyTorch,
  • Random Forest from scikit-learn,
  • Bayesian Networks in pgmpy & Bayesian Classifier from scikit-learn.
    Correlation

    Afterward, K-Means clustering was applied to group the data according to the detected activities. Dendogram