Clustering Technique for Crime Rate Prediction and Warning to Users in Big Data Environment

Crime analysis is a systematic strategy for detecting and evaluating recurring criminal tendencies. Expensed our society significantly in a variety of ways. Many of us have to travel often for work or other reasons, and we constantly encounter a wide variety of threats to our safety, including hijacking, abduction, and harassment. As a whole, we can see that whenever we need to travel somewhere new, the first thing we do is pull up Google Maps; when presented with many routes to our destination, we almost always go for the shortest one, despite our inability to properly assess the situation. In this role, we use a variety of data mining clustering techniques to examine Bangladesh’s crime rate and a K-nearest neighbour (KNN) method to train our dataset, all of which leaves us wondering whether or not we are safe. The two types of information (primary and secondary) are both essential to our work. Through statistical examination, we are able to estimate the forecast rate of various crimes at various locations, and then use this rate to the algorithm’s output to get the predictive accuracy of the route. Last but not least, we use the predicted rate to choose our most secure path. As a result of this work, locals will have a better idea of the area’s safety and can find their way there more efficiently.