Machine Learning based Advanced Crime Prediction and Analysis

One of the society’s most important challenges is crime. It is the most visible part of our civilization. As a result, one of the most crucial jobs is crime prevention. Machine learning approach can better help in the prediction and analysis of the crime. The subject of machine learning crime prediction in India has been addressed through a number of prediction-based theories. Finding the dynamic character of crimes becomes a difficult challenge. The goal of crime prediction is to lower crime rates and discourage criminal activity. In order to discover the proper predictions of crime by using learning-based techniques, this study provides many machine learning algorithms, such as Naive Bayes, Support Vector Machine, Linear Regression, Decision Tree, Bagging Regression, Stacking Regression, and Random Forest Regression algorithms. Comparing the Naive Byes algorithm to other machine learning models such as SVM, bagging, Linear Regression, Decision tree, stacking, and Random Forest, it is used to create configurations that are specific to a certain domain. On the test data, the suggested technique had a classification accuracy of 99.9%. It is discovered that the model has a stronger predictive impact than the earlier one. When compared to baseline studies that just looked at crime data sets based on violence, the model is found to have greater predictive power. The outcomes demonstrated that criminological theories are compatible with any actual evidence on crime. The suggested method was discovered to be helpful for making potential crime predictions.