Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Das, Tanmoy; Joshi, Hemlata Title A name given to the resource Enhancing Education Policy Estimation: A Novel Ridge Fuzzy Regression Approach for Handling Multicollinearity with Fuzzy Input Data Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1231 LNNS;pp.149-160 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-031-78946-5_14" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-78946-5_14</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105007156927?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105007156927?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Das T., Department of Statistics and Data Science, Christ (Deemed to be University), Bengaluru, India; Joshi H., Department of Statistics and Data Science, Christ (Deemed to be University), Bengaluru, India Description An account of the resource Multicollinearity often complicates regression analysis, both in classical and fuzzy input setup. This research introduces a new approach that combines ridge regression with fuzzy regression to tackle correlated covariates impact, with a specific focus on improving education policy systems. Our method utilizes the ?-level estimation algorithm and a dataset where Grade Point Average (GPA) serves as a fuzzy input, while input variables remain crisp. We assess our estimators performance using RMSE and MAPE. This applied research showcases the potential of our method in enhancing education policies through more accurate data-driven decision-making. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. Subject The topic of the resource Education Policy; Multicollinearity; Multiple Fuzzy Linear Regression; Ridge Fuzzy Regression Model; Ridge Regression Model Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303178945-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online