Utilizing Machine Learning Ensemble Techniques for Crime Hotspot Analysis and Prediction

Abstract

By combining the predictions of trained classifiers, the ensemble learning approach generates new examples through cooperative decision-making. Evidence from early analysis has demonstrated the empirical and logical superiority of ensemble classifiers over single component classifiers. It is still difficult to identify the right configuration for a given dataset, even with the presentation of many ensemble approaches. Many theories based on prediction have been developed to address the topic of machine learning crime prediction in India. The dynamic character of crimes becomes difficult to ascertain. Crime prediction aims to lower crime rates and discourage criminal action. This study provides an authentic and efficient way for determining acceptable crime predictions: the assemble-stacking based crime prediction method (SBCPM), which applies learning-based strategies to produce domain-specific configurations compared to another machine learning model. The result implies that performer models are generally not particularly successful. The ensemble model occasionally outperforms the others with the best correlation coefficient, the lowest average, and the lowest absolute errors. The proposed method generated accurate categorization on the test data. Compared to previous research that just employed violence-based crime records as a baseline, the model's prediction effect is demonstrated to be stronger. The results further shown that criminological theories are congruent with any real-world crime data. The recommended strategy also proved useful in predicting possible crimes. and show that the ensemble model has higher prediction accuracy when compared to the individual classifier.