Crime Type and Occurrence Prediction Using Machine Learning Algorithm

Abstract

This project is entitled as “Crime Type and Occurrence Prediction Using Machine Learning Algorithm” Crime is still a major worry and a serious problem in our society. As a result, crime prevention is a significant issue that needs to be examined methodically. Detecting and preventing crime requires effective crime analytics, which is also crucial for assessing how well criminal investigations are working. To create precise forecasts, inferences are drawn from trained data. a system that uses user input to forecast different characteristics of crime. Users select the year, offense type, and city name. The method forecasts the population, expected number of occurrences, and expected crime rate for the year in 100,000 units based on this data. The dataset used in this investigation was produced on its own. The system makes use of machine learning techniques, particularly Random Forest, SVM, Logistic Regression, and Linear Regression. To guarantee precise forecasts and dependable results, it has undergone testing and training