Development of a Module for Evaluating the Activity of the Mahalla Chairpersons Based on the Experts' Assessment with the Help of Machine Learning Algorithms
Annotatsiya
This paper is delved to deeply into the integration of machine learning algorithms for the creation of an evaluative module dedicated to assessing the activities of mahallas, using employment data sourced from these mahallas as a primary metric. In the context of machine learning-based classification challenges, this research highlights the methodical development of a model that incorporates and leverages a trio of key machine learning algorithms: Linear Regression, Polynomial Regression, and Neural Networks. By strategically implementing these algorithms, the intricate problem of autonomously evaluating mahallas has been effectively addressed and solutions have been delineated. To ensure a robust experimental framework, all analytical and computational processes were executed on the KNIME Analytics platform, a renowned tool in the data analytics domain. After rigorous testing, the outcomes derived from each of the aforementioned algorithms were juxtaposed to draw insightful comparisons. The culmination of the study sees an articulate presentation of findings, offering conclusions and potential implications for the broader field of machine learning in community assessment.