Analysis and Prediction of Crime Hotspots Using Machine Learning With Stacked Generalization Approach

Wherever the crime rate is exceptionally high, we call that a crime hotspot. It is common practice to use a map to illustrate them. In order to generate new examples, the ensemble learning technique uses a cooperative decision-making method that aggregates predictions of trained classifiers. Early study has demonstrated that ensemble classifiers are both theoretically and empirically more trustworthy than any single part classifier. Even if a variety of ensemble approaches are offered, it might be challenging to identify the best configuration for a given dataset. Numerous prediction-based theories have been used to solve the issue of machine learning crime prediction in India. Discovering the ever-changing nature of criminal acts becomes an arduous undertaking. Crime prediction seeks to reduce crime rates and determine illegal Behaviour. Compared to another machine learning models, relevant predictions of crime can be made by adopting learning-based methodologies used to produce configurations unique to a certain domain. The analysis conclusion indicates that a performer model rarely operates as intended. The model is found to have higher prediction value when compared to baseline studies that only examined crime records based on violence and claim that the accuracy of predictions with stacking ensemble model is greater than that of individual classifier.