Chicago Crime Classification
My most recent project was a group project as part of the first term of the COMPASS CDT. It involved modelling different aspects of a large data set detailing crimes in Chicago. Pictured is a kernel density estimate of both the location of crimes in the city as well as the population.
My input to this project was to use logistic regression methods to classify whether an arrest would be successful or not, given other covariates in the crime set; detailing the location, the area, the type of crime type, amongst others. This involved feature engineering and using LASSO coefficient paths to judge the most ‘important’ covariates.
The package that was created for this project can be found as a Github repository here.
Daniel Williams
CDT Student
I have a PhD in statistics/machine learning/data science/AI (whatever you would like to call it) from the University of Bristol, under the COMPASS CDT. I previously studied a masters in mathematics at the University of Exeter. My research was primarily on truncated density estimation and unnormalised models. But I am also interested in AI more generally, including all the learnings, Machine, Deep and Reinforcement (as well as some others!).