Analysis and Prediction of Crime Using Machine Learning

Preventing crime is a key job because it is one of our society's severe and prevalent issues. A vast number of crimes are committed every day. It's crucial to keep track of all offences and create a database for future use. The current problem we're dealing with is keeping a proper crime record and analysing that data to help forecast and resolve future crimes. The motive of our project is to examine a collected information set including several criminal activities and predict the kind of crime Activity arises in the time ahead based on a variety of factors. For this project, we will use machine learning to forecast crime. We're looking at crime statistics from across the United States and collected the data from kaggle in the form of CSV format. The data gathered is a United States data. It holds information on the criminal activity, such as the spot, sort of offence, latitude, time and longitude. For analysing, the ARIMA model is used. For crime prediction, Decision tree algorithm and various other algorithms will be tried, and the one with the best accuracy will be used for training. The goal of this project is to demonstrate how law enforcement agencies may employ machine learning and criminal analysis to detect, anticipate, and solve crimes at a significantly faster pace, lowering crime rates.