Crime Type and Occurrence Prediction in Machine Learning Algorithm

Ensemble learning method is a collaborative decision-making mechanism that implements to aggregate the predictions of learned classifiers in order to produce new instances. Early analysis has shown that the ensemble classifiers are more reliable than any single part classifier, both empirically and logically. While several ensemble methods are presented, it is still not an easy task to find an appropriate configuration for a particular dataset. Several prediction-based theories have been proposed to handle machine learning crime prediction problem. It becomes a challenging problem to identify the dynamic nature of crimes. Crime prediction is an attempt to reduce crime rate and deter criminal activities. This work proposes an efficient authentic method called assemble-stacking based crime prediction method (SBCPM) based on algorithms for identifying the appropriate predictions of crime by implementing learning-based methods applied to achieve domain-specific configurations compared with another machine learning model. The result implies that a model of a performer does not generally work well. In certain cases, the ensemble model outperforms the others with the highest coefficient of correlation, which has the lowest average and absolute errors. The proposed method achieved classification accuracy on the testing data. The model is found to produce more predictive effect than the previous researches taken as baselines, focusing solely on crime dataset based on violence.

 Keywords: Decision tree, Random Forest, Logistic Regression and Machine learning techniques