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@article{175744,
        author = {Abinaya J},
        title = {A Data-Driven Model for Predicting Task Assignments in Agile Teams Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4005-4009},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175744},
        abstract = {In modern agile project management, the manual assignment of tasks across various tools can lead to inefficiencies and resource misallocation. This project explores the use of machine learning to predict task assignees based on historical data from multiple project tracking tools such as Jira, Azure DevOps, Jira Align, Trello, and Smartsheet's. The objective is to develop an intelligent model that automates task assignment, enhancing team productivity and reducing manual effort.
The study involves collecting and preprocessing historical data from these tools to build a predictive model. Initially, the Random Forest algorithm will be used due to its effectiveness in handling complex datasets. However, the model's accuracy will be evaluated, and other algorithms may be explored to optimize predictions. This approach aims to streamline task allocation across different tools, improving team efficiency and overall project management.},
        keywords = {Task Assignment Prediction, Machine Learning in Agile, Random Forest Algorithm, Task Automation, Assignee Prediction Model, Feature Engineering in ML, Categorical Data Encoding, TF-IDF Vectorization, Project Management Automation, Predictive Analytics in DevOps},
        month = {April},
        }