Perfecting the Crime Machine
This is an incremental application of standard ML methods to crime prediction in a specific city.
The study tackled crime type prediction in Philadelphia using location and time features, finding that Random Forest performed best with a log loss error of 2.3120.
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.