• K. Mahima Chowdary
• M. Vaishnavi
• A. Saketh Rao
• B. Hemanth
• Dr. M. Padmaja

The ensemble learning approach is a cooperative dynamic technique that creates new models by using the expectations of learned classifiers. Tentatively and hypothetically, early exploration has shown that gathering classifiers are more dependable than single-part classifiers. Despite the fact that a ton of gathering techniques have been portrayed, it's still difficult to come by the best arrangement for a specific dataset. In India, various forecast-based speculations have been introduced to resolve the issue of ML crime expectation. It becomes challenging to decide the unique idea of crimes. The target of bad behavior assumption is to restrict crime rates and dissuade crime. Assemble-stacking-based crime prediction method (SBCPM), in light of SVM calculations, is introduced in this paper as a compelling genuine technique for coordinating learning-based techniques in MATLAB to choose satisfactory crime expectations. The SVM technique is utilized to make area explicit arrangements as opposed to the Random Forest, SMO Naive Bayes bagging, and another machine learning model called J48. Therefore, an entertainer model for the most part neglects to really work. The troupe model has the most noteworthy relationship coefficient and the least normal and outright blunders when contrasted with different models in certain circumstances. The proposed strategy had the option to characterize the testing information with 99.5% accuracy. It has been demonstrated that the model has greater predictive power than previous studies that focused solely on violent crime datasets. Additionally, the results demonstrated that criminological concepts and any empirical data on crime can be reconciled. The recommended strategy was likewise demonstrated to be advantageous for expecting possible violations.

Random Tree Algorithm, K-Nearest Neighbor (KNN), Bayesian model, Support Vector Machine (SVM), and Neural Networks.

"Crime rate prediction and analysis using machine learning algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.g25-g29, March-2023, Available :http://www.jetir.org/papers/JETIR2303604.pdf

"Crime rate prediction and analysis using machine learning algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppg25-g29, March-2023, Available at : http://www.jetir.org/papers/JETIR2303604.pdf