Crime Detection Approach Using Big Data Analytics and Machine Learning

The Crime Analysis phase establishes the number of crimes and other elements such as the type of crime, murder, rape, kidnapping, etc. Various data analytics methodologies used in security and criminal investigation have shown the evolution of illegal analytics over the previous three decades. First, we'll go through the various data mining tools, such as text mining, neural networks, and machine learning. Then we'll look at their recent uses in criminal analytics and the challenges that arise. Supervised machine learning classification models have been developed and applied for predictive modelling. This article uses big data and computational intelligence to forecast violent crime. This study aimed to categorize the crime prediction technique into five classes, each reflecting a different sort of crime, and assess its accuracy. MATLAB explores simple learning methods like naive byes, random tree classifiers, and meta-classifiers. 96.6% The meta-model's accuracy is that data mining outperforms another model in forecasting violent crime.