• Priyanka Jadhav
• Amruta Ladgaonkar
• Kaivalya Patil
• Shivani Dive
• Prof. Nakul Sharma

Increasing in Criminal activities adversely affects any country’s economic growth including quality of life of the peoples. There has been enormous growth in crime rates in recent years. Recognizing the patterns of criminal activity of a place is paramount in order to prevent it. Many governments are trying to use advanced techniques to tackle such issues. The aim of the study is to identify the crime rate and solve crimes faster based on the data collected. The relationships exist among the various crime types and crime Variables. Scrutinizing this data set can offer insight into criminal activities. Here we are using a random forest algorithm to build a prediction model for identifying the various crimes. The comparison of various Supervised Learning Algorithm i. e Support Vector Machine, KNN, Decision tree, and Naïve Bayesian is done against Random forest.

Crime Rate Analytics, Machine eLearning Approach, Crime Prevention, crime types, prediction model, Data Collection, Dependent Variable Analysis, Word2Vector Formation, and Algorithm Analysis.

"Machine Learning-Based Predictive Modeling Approach for Criminal", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.a115-a121, June-2021, Available :http://www.jetir.org/papers/JETIR2106016.pdf

"Machine Learning-Based Predictive Modeling Approach for Criminal", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppa115-a121, June-2021, Available at : http://www.jetir.org/papers/JETIR2106016.pdf