This repository contains a project that compares and contrasts the algorithmic / ML approach to data analysis with the classical statistical modeling approach.
It contains
- an article that gives a detailed report on the topic;
- the R code required to reproduce the classical statistical modeling results;
- a Jupyter notebook for reproducing the ML results;
- the original data for reproducing the analysis in full in the file
./data/seizures_original.csv
; - three transformed data sets so that you can pick up at the modeling stage without having to re-run feature extraction;
- the features extracted for the Python Jupyter notebook in the file
./data/seizures_features.csv
; - the features extracted for the R code in the file
./data/fdata.csv
; - autocovariance data produced as an intermediate feature extraction step (for the R file) in the file
./data/AC.csv
.
- the features extracted for the Python Jupyter notebook in the file
- finally, the repository contains the python package requirements in
requirements.txt
reproducibility.