This project is a framework for the analysis of user journey data in the context of internet-based interventions. It includes functions for the creation of the basic data structure for handling user journeys, the creation of features that can add additional information to the existing raw data, and it ultimately provides a framework for the statistical analysis.
Link: https://github.com/VBremer/UJ-Analysis
This project analyzes how consumers respond to organic and paid search advertising. Insight is gained into the interrelationship between organic and paid search advertising by analyzing a dataset using a binary logit model and Markov-Chain-Monte-Carlo techniques.
Link: https://github.com/VBremer/OnlineMarketing
This project aims at predicting self-esteem based on Ecological Momentary Assessment data. Models for ordinal outcomes with varying levels of patient individual parameters are applied.
Link: https://github.com/VBremer/PredictSelfEsteem
Wrapper for the execution of multiple machine learning models including k-fold cross validation. Adjusted example from machinelearningmastery.com.