International Journal of Science, Engineering and TechnologyAuthors: Research Scholar Shraddha J. Pawar, Baisa L. Gunjal
Abstract: Crime forecasting has now acquired more signifi- cance in improving security and directing constabulary policies. This study will examine the current state of the art in crime pre- diction using Machine Learning techniques, specifically looking at classifiers such as linear regression, logistic regression, Neural Networks, Ensemble Methods, and Hybrid Frameworks. The paper presents the most important aspects of the approaches of analyzing the data, including data preprocessing, feature selection, evaluation of the models and algorithms, and applications. Part of the problems that are coming out in the development of this technology are touched upon briefly, such as data bias, privacy and scalability and solutions are given. The results indicate that there are certain trends as multimodal integration and spatial-temporal models, which have led to the change in the prediction accuracy.
