Predictive Analysis of Crime Rates Using Machine Learning Algorithms

This study outlines a leader-edge technique that machine learning introduces in crime prediction over traditional models. In the face of intensifying urbanization and escalating population growth which is completely changing the societies, it is a core of grasp and have a foresight of crime patterns that law enforcement agencies and the policymakers should focus on. This study captures diverse features comprising demographic data, historical crime records, socioeconomic indicators, and geographic information among others, to develop comprehensive data inputs into the advancements in machine learning algorithms which in turn compute the predictive models. The technique comprising of data preprocessing, feature engineering, and model building is using a plethora of algorithms including decision trees, random forests, support vector machines, and neural networks. Another aspect of paper is to explain why it is important for feature selection and hyperparameter tuning in improving the model's performance.