jaketuricchi
PhD researcher in weight management with a focus on tracking technologies
University of LeedsLeeds, UK
Pinned Repositories
BodyWeight_Imputation_Validation_Variability
Script associated with the publication: https://mhealth.jmir.org/preprint/17977. It describes how to deal with missing data, and the impact of missingness on linear and non-linear calculation of body weight variability, which may be an important marker in health epidemiology.
2015
Public material for CS109
2015lab1
2015lab2
KaggleScripts
Scripts in R and Python used for Kaggle competitions
My-Deeplearning.ai-Path
My Deeplearning.ai notebooks
NoHoW-Weight-data-processing
Publication_scripts
Scripts associations with published studies
PythonForMachineLearning
PythonForMachineLearning
streamlit
Streamlit — The fastest way to build data apps in Python
jaketuricchi's Repositories
jaketuricchi/streamlit
Streamlit — The fastest way to build data apps in Python
jaketuricchi/KaggleScripts
Scripts in R and Python used for Kaggle competitions
jaketuricchi/Publication_scripts
Scripts associations with published studies
jaketuricchi/2015lab2
jaketuricchi/2015lab1
jaketuricchi/Weight_fluctuation_Fitbit_Aria_NoHoW
Script associated with the analysis of data collected from >1200 smart scale users as part of a recent pan-European trial; The NoHoW trial.
jaketuricchi/BodyWeight_Imputation_Validation_Variability
Script associated with the publication: https://mhealth.jmir.org/preprint/17977. It describes how to deal with missing data, and the impact of missingness on linear and non-linear calculation of body weight variability, which may be an important marker in health epidemiology.
jaketuricchi/My-Deeplearning.ai-Path
My Deeplearning.ai notebooks
jaketuricchi/PythonForMachineLearning
PythonForMachineLearning
jaketuricchi/NoHoW-Weight-data-processing
jaketuricchi/2015
Public material for CS109