  This study explores using different machine learning techniques and workflows
to predict crime related statistics, specifically crime type in Philadelphia.
We use crime location and time as main features, extract different features
from the two features that our raw data has, and build models that would work
with large number of class labels. We use different techniques to extract
various features including combining unsupervised learning techniques and try
to predict the crime type. Some of the models that we use are Support Vector
Machines, Decision Trees, Random Forest, K-Nearest Neighbors. We report that
the Random Forest as the best performing model to predict crime type with an
error log loss of 2.3120.
