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To ensure the security of the general mass, crime prevention is one of themost higher priorities for any government. An accurate crime prediction modelcan help the government, law enforcement to prevent violence, detect thecriminals in advance, allocate the government resources, and recognize problemscausing crimes. To construct any future-oriented tools, examine and understandthe crime patterns in the earliest possible time is essential. In this paper, Ianalyzed a real-world crime and accident dataset of Denver county, USA, fromJanuary 2014 to May 2019, which containing 478,578 incidents. This project aimsto predict and highlights the trends of occurrence that will, in return,support the law enforcement agencies and government to discover the preventivemeasures from the prediction rates. At first, I apply several statisticalanalysis supported by several data visualization approaches. Then, I implementvarious classification algorithms such as Random Forest, Decision Tree,AdaBoost Classifier, Extra Tree Classifier, Linear Discriminant Analysis,K-Neighbors Classifiers, and 4 Ensemble Models to classify 15 different classesof crimes. The outcomes are captured using two popular test methods: train-testsplit, and k-fold cross-validation. Moreover, to evaluate the performanceflawlessly, I also utilize precision, recall, F1-score, Mean Squared Error(MSE), ROC curve, and paired-T-test. Except for the AdaBoost classifier, mostof the algorithms exhibit satisfactory accuracy. Random Forest, Decision Tree,Ensemble Model 1, 3, and 4 even produce me more than 90% accuracy. Among allthe approaches, Ensemble Model 4 presented superior results for everyevaluation basis. This study could be useful to raise the awareness of peoplesregarding the occurrence locations and to assist security agencies to predictfuture outbreaks of violence in a specific area within a particular time.

A Comparative Study on Crime in Denver City Based on Machine Learning and Data Mining