The research project focused on the development of an algorithm aimed at automating incident categorization. The primary objective was to create a robust and intelligent system capable of classifying incidents into predefined categories accurately. By analyzing historical incident data, identifying patterns, and leveraging machine learning techniques, the project aimed to develop an algorithm that could automatically assign incidents to appropriate categories based on their characteristics and attributes. The algorithm's goal was to reduce the manual effort and subjectivity involved in incident categorization, leading to more consistent and efficient incident management. Through iterative training and validation, the algorithm aimed to continually improve its accuracy and adaptability to new incident types. The project aimed to bridge the gap between manual categorization and automated classification, contributing to streamlined incident management processes and enhanced decision-making within the organization.

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