PhD Thesis Data and Code for Chapter 8

This storage contains files described in a PhD thesis titled 'Personalisation of a DHBCI for midlife women in the UK', by Hana Sediva, dated 24/5/2024.  The files correspond to Chapter 8 titled 'Predicting Health Behaviours in UK-Residing Midlife Women Using Machine Learning with Ecological Momentary Assessment and Fitness Tracker Data: An Exploratory Study'.

The storage contains:

1) Intervention dataset generated programatically in R and used in all ML analysis (ThesisMLDataFile.csv)

2) Time-varying predictors used to access the dataset in Python (dfPredictors.csv)

3) Weighted spearman correlation file generated in R (csv)

4) Python code for feature selection created in Jupyter Notebook (.ipynb)

5) Python code and results for feature selected in PDF (.pdf)

6) R code for feature selection created in R studio as R Markdown (.rmd)

7) R code for feature selection in PDF (.pdf)