Crime rate prediction using machine learning

The context of crime analysis, the investigative process is often time-consuming, particularly in developing countries like India. This research aims to explore techniques that can facilitate the prediction of prevalent crime types in specific regions. Crime is a harmful and social issue that affects individual people all around the world. The rate of crime has risen extremely in recent years and going to be more in upcoming years. The purpose of this study is to provide the police department and people with proper crime forecasting so they can better delegate their resources in response to future crime hot spots. Therefore, this research paper proposes machine learning algorithm to detect the crimes based on the data collected and to show the extent of crime in a particular areas. Previous studies are lacking to achieve crime forecasting and prediction accuracy based on learning models. Therefore, this study applied different machine learning algorithms, namely, the k-nearest neighbors (KNN), decision tree, support vector machine (SVM), eXtreme Gradient Boosting (XGBoost) and random forest, the predictable and totally the algorithm which furnish excessive accuracy is be chosen for evaluation the result.