Prajvalarani Sushil Kumar Bogar
Computer Science
June 2025
As a society, we are deeply troubled by the prevalence of crime. There are a lot of crimes perpetrated every day, and the regular occurrence of these crimes has made people anxious. As a result, stopping the crime from happening is an important responsibility. Artificial intelligence has recently proven its worth in nearly every industry, including crime prediction. But, for reference purposes in the future, it is necessary to have an accurate database of the crimes that have taken place. Law enforcement authorities can be better prepared to prevent crimes before they happen if they can anticipate the kind of crimes that might occur in the future. From a strategic standpoint, law enforcement could benefit from the ability to forecast any crime based on time, place, and other factors. But with crime rates rising at such a frightening pace, reliable crime prediction is proving to be a formidable obstacle. As a result, techniques for crime prediction and analysis are crucial for spotting and reducing criminal activity in the future. Recently, a lot of people have been trying to figure out how to use machine learning and specific inputs to make crime predictions. Decision trees, KNN, and other algorithms are utilized for crime prediction. Two types of data are being used in our job: primary and secondary. We utilize the method to ascertain the path's prediction rate after evaluating the data to figure out, for many locations, the prediction rate of various crimes. Lastly, we utilize the projected rate to determine our safe route. People will learn about the dangerous area and how to safely get to their destination with the help of this job.
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