• Mr. J. Murali
• G.Mahendra
• G.V.Sunil Kumar
• S.Shobha
• K.Yasini

In this project, we aim to explore the interaction between crime rates and road safety issues using the large geographic data provided by the Google Maps API. The SVM model is trained on historical crime data as well as road safety indicators obtained from Google Maps, including accident history, lighting conditions and traffic density. By analyzing these factors and crime patterns, we aim to uncover relationships and create a predictive framework that can predict crime rates in different geographic areas. In addition, we conduct a comprehensive evaluation to assess the accuracy and robustness of the model, and consider the potential benefits of incorporating road safety considerations into crime prediction methods. This research can inform targeted interventions and resource allocation strategies aimed at reducing crime rates and improving public safety outcomes.

PREDICTION OF CRIME RATE USING SVM MODEL WITH INCLUDING OF GOOGLE MAPS FOR ROAD SAFETY

"PREDICTION OF CRIME RATE USING SVM MODEL WITH INCLUDING OF GOOGLE MAPS FOR ROAD SAFETY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.m29-m36, April-2024, Available :http://www.jetir.org/papers/JETIR2404C05.pdf

"PREDICTION OF CRIME RATE USING SVM MODEL WITH INCLUDING OF GOOGLE MAPS FOR ROAD SAFETY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppm29-m36, April-2024, Available at : http://www.jetir.org/papers/JETIR2404C05.pdf