  The aim of this study was to approach a difficult regression task on highly
unbalanced data regarding active theater of war in Afghanistan. Our focus was
set on predicting the negative events number without distinguishing precise
nature of the events given historical data on investment and negative events
per each of predefined 400 Afghanistan districts. In contrast with previous
research on the matter, we propose an approach to analysis of time series data
that benefits from non-conventional aggregation of these territorial entities.
By carrying out initial exploratory data analysis we demonstrate that dividing
data according to our proposal allows to identify strong trend and seasonal
components in the selected target variable. Utilizing this approach we also
tried to estimate which data regarding investments is most important for
prediction performance. Based on our exploratory analysis and previous research
we prepared 5 sets of independent variables that were fed to 3 machine learning
regression models. The results expressed by mean absolute and mean square
errors indicate that leveraging historical data regarding target variable
allows for reasonable performance, however unfortunately other proposed
independent variables does not seem to improve prediction quality.

Machine Learning;Machine Learning