• Pranali Manwar
• Sushma Shinde

The global rise in criminal activities necessitates a concerted effort to lower crime rates, as they have a direct impact on a nation's social and economic progress. Consequently, there is an immediate requirement for security agents and agencies to combat and diminish crime within society. Additionally, by creating efficient crime detection systems, law enforcement agencies can actively respond to incidents, thwarting criminal acts and safeguarding communities. The proposed system helps to predict the type of crime in a particular region of the Chicago based on patterns of criminal activity. Random forest is most accurate algorithm providing maximum accuracy of 90.23% in predicting the crime rate based on different factors like crime date, location, time, crime type. domestic, ward, description.

Crime rate prediction, Machine learning, Random Forest, Crime type, Crime location, Crime type, Location

"Crime Rate Prediction using Machine Learning - A Predictive Framework", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.h1-h8, April-2024, Available :http://www.jetir.org/papers/JETIR2404701.pdf

"Crime Rate Prediction using Machine Learning - A Predictive Framework", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. pph1-h8, April-2024, Available at : http://www.jetir.org/papers/JETIR2404701.pdf