Criminal Incidence Prediction

Crime is one of the major problems we face in our society, and its prevention is critical. The number of crimes has increased enormously over the past decade. It is imperative to keep a track of criminal activity around us. In this study, crime hotspots were identified and predicted based on a large number of datasets derived from past criminal records. Recent advances in machine learning techniques have made this possible. Random Forest and AdaBoost algorithms were used in this study to analyze crime rates in society. A crime data set from Kaggle, an open-source platform, was used for crime analysis. These machine learning algorithms were used to reduce crime rates.