| Detecting and Predicting Crimes using Data Mining Techniques: Comparative Study |
| Paper ID : 1031-ICCI2021 (R1) |
| Authors: |
|
samah samir zaheran *1, Eman M Mohamed1, hamdy mohamed mousa2 1computer science, Faculty of computers and information, Menofia University, Egypt. 2Computer Science Department Faculty of Computers and Information Menoufia University |
| Abstract: |
| Crime is a major problem where the top priority has been concerned by individuals, the community, and the government. Thus, it seems important to study factors and relations between the occurrence of different crimes to avoid more upcoming crimes. crime forecasting is a way of trying to study the causes of crime and predict the times and places of its occurrence this is to reduce the crimes that are expected to occur in the future. Data mining methods are too important to resolve the crime problem by investigating hidden crime patterns. so, this study aims to analyze and discuss the various methods that are applied to predict future crime and analyze its results. In this study, the technique of crime prediction is proposed which is based on some classification algorithms such as (NB, KNN, Decision Tree, random forest, Linear Regression, Logistic Regression, SVM), these classification algorithms are applied to four real data sets (Chicago dataset, Los Angeles dataset, Egypt dataset, United States dataset), Egypt data set was extracted primarily from the online website (Zabatak.com) and comparing between their scores. the experimental results showed that the Random Forest classifier achieves a high score on four data sets compared with other classifiers. Random Forest achieves %88 on the Los Angeles dataset, %92 on the Egypt dataset, %97 on the Chicago dataset, and 81.7% on the United States dataset. |
| Keywords: |
| Crime; KNN; NB; SVM. |
| Status : Paper Accepted |
