• Sadum Bharath Kishore
• K. Suresh

Crime poses a significant threat to societal stability and individual safety, impacting economic growth and the quality of life. The increasing complexity and frequency of crimes challenge traditional methods of crime analysis and prediction, which often lack the accuracy and efficiency required for proactive measures. Existing approaches frequently fail to address the non-linear and dynamic nature of crime data, leading to suboptimal predictive performance. To overcome these limitations, we propose an ensemble machine learning model that integrates Gradient Boosting and XGBoost through a voting strategy to enhance predictive accuracy. The hyper parameters of both models are fine-tuned using Randomized Search CV to optimize their performance and ensure robust predictions. This proposed system aims to assist law enforcement agencies in better understanding crime patterns, enabling data-driven decisions for crime prevention and resource allocation. The integration of advanced machine learning techniques demonstrates the potential for improved precision and reliability in crime prediction systems.

A ENHANCING CRIME PREDICTION USING ENSEMBLE MACHINE LEARNING MODELS

"A ENHANCING CRIME PREDICTION USING ENSEMBLE MACHINE LEARNING MODELS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.e218-e225, March-2025, Available :http://www.jetir.org/papers/JETIR2503439.pdf

"A ENHANCING CRIME PREDICTION USING ENSEMBLE MACHINE LEARNING MODELS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppe218-e225, March-2025, Available at : http://www.jetir.org/papers/JETIR2503439.pdf