A tutorial for data cleaning and model building in Python
This repository contains code based on a group project alongisde Tze Ni Yeoh, Abdula Saif, Lucas Kitzmüller and Victor Sheng.
The challenge that we have chosen as an example is to predict the spread of Ebola by region during the 2014-2016 outbreak in Sierra Leone.
The code walks through different steps in cleaning data, feature engineering, training machine learning models and optimizing parameters.
The goal for the models is to predict Ebola outbreaks, both the presence and total number of cases.
The Western African Ebola virus epidemic (2014–2016) caused 11,325 deaths and major socioeconomic disruption, with a majority of fatalities taking place in the coastal nation of Sierra Leone. During the outbreak, national authorities had enough resources to isolate and treat all reported cases and stop further transmission of the virus. Unanticipated local variation in the total number of incidences, however, created insufficient response capacity in certain districts (WHO Situation Report, 2014).
There is a need for a complementary tool in informing the allocation of resources across districts and increasing response effectiveness in current and future Ebola emergencies.