Building Model for Crime Pattern Analysis Through Machine Learning Using Predictive Analytics

Crime has a big impact in both the human lives and society’s growth, which needs to be addressed controlled. Machine learning algorithms as fanciest technology assist decision makers policy making proven its reliability showing unseen patterns crime. This research aims examine capability of trees ensemble classifying crime through model development. Experiments were done enhance ensembles classification regression. Feature extraction like synthetic minority oversampling technique was applied order address problem imbalanced data. Different metrics relevant regression considered evaluating performance each used. With use different metrics, Gradient boosted tree found have better dataset after outperforming random forest problem. Furthermore, also promising by Therefore, it is highly recommended that this algorithm further examined developing other datasets.