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@article{194457,
        author = {Durga. C and Dr. Sreejith Vignesh B P},
        title = {FORECASTING CYBERCRIME USING MACHINE LEARNING ALGORITHMS IN PYTHON},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {4150-4157},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194457},
        abstract = {Cybercrime has increased dramatically as a result of the fast development of digital technologies, which represents a serious risk to global security. Traditional reactive methods frequently fall behind the changing nature of cyberattacks. Utilizing Python within the Google Colab cloud environment and implementing cutting-edge Machine Learning (ML) algorithms, this study seeks to predict cybercrime trends. Using a dataset of local cyber events, the research uses Multi-Output Regressors and Random Forest Classifiers to discover underlying patterns and forecast particular future threats. The study shifts from simple detection to predictive analytics, providing a scalable framework for businesses and law enforcement. The results show that integrating web-based frontends (HTML/CSS/JS) with ensemble-based learning produces an accessible and extremely precise approach to proactively reduce risk.},
        keywords = {Cybercrime, Forecasting, Machine Learning, Python, Google Colab, Predictive Modeling, Random Forest.},
        month = {March},
        }