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@article{202155,
        author = {S P Preethi and Dr. Murthy SVN},
        title = {Crime Type and Occurrence Prediction Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {6231-6242},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202155},
        abstract = {Crime prediction has emerged as a vital aspect research area in the field of data science and artificial intelligence owing to the rise of increasing availability of digital crime records and the growing demand for proactive law enforcement strategies. Fast-paced urban expansion, rising demographic growth, and socio- economic disparities have significantly contributed to the rise and diversification of criminal activities. Traditional crime analysis approaches, which predominantly utilize manual examination of records and descriptive statistical techniques, are often inadequate for analyzing large-scale datasets and identifying complex hidden patterns.
Machine learning offers robust analytical mechanisms to analyze historical crime data, learn underlying trends, and predict crime types and their likelihood of occurrence under different conditions. This survey paper presents an in-depth review of existing crime prediction methodologies using machine learning techniques. The paper discusses data sources, preprocessing strategies, feature engineering approaches, classification and ensemble models, and evaluation metrics used in crime prediction research. In addition, it highlights major challenges such as data imbalance, spatio-temporal variability, bias in crime datasets, interpretability of models and associated ethical considerations in predictive policing. The survey also explores practical applications and future research directions, emphasizing explainable artificial intelligence, real-time crime analytics, and responsible deployment. This work intends to offer an extensive groundwork for researchers and practitioners working in developing reliable and scalable crime prediction systems.},
        keywords = {Crime Prediction, Machine Learning, Predictive Policing, Classification Models, Data-Driven Decision Making.},
        month = {May},
        }