Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Machine Learning Algorithms for Visualization and Prediction Modeling of Boston Crime Data

Version 1 : Received: 8 February 2020 / Approved: 9 February 2020 / Online: 9 February 2020 (16:02:03 CET)

How to cite: Yin, J.; Afa Michael, I.; Afa, I.J. Machine Learning Algorithms for Visualization and Prediction Modeling of Boston Crime Data. Preprints 2020, 2020020108 (doi: 10.20944/preprints202002.0108.v1). Yin, J.; Afa Michael, I.; Afa, I.J. Machine Learning Algorithms for Visualization and Prediction Modeling of Boston Crime Data. Preprints 2020, 2020020108 (doi: 10.20944/preprints202002.0108.v1).

Abstract

Machine learning plays a key role in present day crime detection, analysis and prediction. The goal of this work is to propose methods for predicting crimes classified into different categories of severity. We implemented visualization and analysis of crime data statistics in recent years in the city of Boston. We then carried out a comparative study between two supervised learning algorithms, which are decision tree and random forest based on the accuracy and processing time of the models to make predictions using geographical and temporal information provided by splitting the data into training and test sets. The result shows that random forest as expected gives a better result by 1.54% more accuracy in comparison to decision tree, although this comes at a cost of at least 4.37 times the time consumed in processing. The study opens doors to application of similar supervised methods in crime data analytics and other fields of data science

Supplementary and Associated Material

http://www.dropbox.com/s/7r05fag4z4vhsh9/Boston_Crime.zip?dl=0: Shapefiles codes, Modeling Codes in R and data folder

Subject Areas

machine learning; decision tree; random forest; crime data analytics

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