A Comprehensive Review on Crime Patterns and Trends Analysis using Machine Learning
Aryan Ratra, Aryan Agarwal, Satvik Vats, Vikrant Sharma, Vinay Kukreja, Satya Prakash Yadav
Abstract
This study examines the analysis and visualization of a dataset from the London Police that contains crime incidents reported over a 12-month span. The dataset contains several columns, including the last police-reported result, the month the crime was reported, location coordinates, and the type of crime. In order to gain insight into the dataset, the research makes use of a variety of data analysis and visualization techniques, including scatter plots, frequency distributions, heatmaps, K-means clustering, random forest classification, and chi-squared tests. Based on the reported department and last outcome category, the findings indicate that crime incidents are concentrated in particular regions and differ greatly from one another. The research emphasizes the potential of data analysis and visualization methods in crime analysis and offers useful information for policymakers and law enforcement organizations to better comprehend and address crime in London.