Crime Prediction and Analysis

Abstract

The systematic technique used to detect and analyze crime patterns and trends is called crime analysis and prevention. The proposed model will be able to predict regions that have a high chance of crime occurrence. Crime data specialists can assist police officers in stopping crime more quickly. Our goal is to develop methods that can forecast homicide activities based on demographic and economic data from a specific area. In this project, we use a classification and clustering algorithm to build an effective crime prediction model. Support vector networks, multivariate time- series data, and artificial neural networks are some of the prediction approaches that can be used to test the efficiency of prediction models. We use multiple algorithms for classification and clustering and then we compare with accuracy. To measure the out-oftraining effectiveness of classifiers, we use a ten- fold cross-validation approach.