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 Conference Papers 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/. Title A name given to the resource Towards an Improved Model for Stability Score Prediction: Harnessing Machine Learning in National Stability Forecasting Subject The topic of the resource and security; Economy politics; Fragile State of Index; Machine Learning Description An account of the resource In our increasingly interconnected world, national stability holds immense significance, impacting global economics, politics, and security. This study leverages machine learning to forecast stability scores, essential for understanding the intricate dynamics of country stability. By evaluating various regression models, our research aims to identify the most effective methods for predicting these scores, thus deepening our insight into the determinants of national stability. The field of machine learning has seen remarkable progress, with regression models ranging from conventional Linear Regression (LR) to more complex algorithms like Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting (GB). Each model has distinct strengths and weaknesses, necessitating a comparative analysis to determine the most suitable model for specific predictive tasks. Our methodology involves a comparative examination of models such as LR, Polynomial Regression (PR), Lasso, Ridge, Elastic Net (ENR), Decision Tree (DT), RF, GB, K-Nearest Neighbors (KNN), and SVR. Performance metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared (R2) assess each model's predictive accuracy using a diverse dataset of country stability indicators. This study's comprehensive model comparison adds novelty to predictive analytics literature. Our findings reveal significant variations in the performance of different regression models, with certain models exhibiting exceptional predictive accuracy, as indicated by high R2 values and low error metrics. Notably, models such as LR, SVR, and Elastic Net demonstrate outstanding performance, suggesting their suitability for stability score prediction. 2024 IEEE. Creator An entity primarily responsible for making the resource Maheshwari A.; Malhotra A.; Singh Hada B.; Ranka M.; Amzad Basha M.S. Source A related resource from which the described resource is derived Proceedings of NKCon 2024 - 3rd Edition of IEEE NKSS's Flagship International Conference: Digital Transformation: Unleashing the Power of Information Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/NKCon62728.2024.10775198" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/NKCon62728.2024.10775198</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215302501&amp;doi=10.1109%2FNKCon62728.2024.10775198&amp;partnerID=40&amp;md5=5e4e15ab92a68efcf33ab4fdf2362ea5" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215302501&amp;doi=10.1109%2fNKCon62728.2024.10775198&amp;partnerID=40&amp;md5=5e4e15ab92a68efcf33ab4fdf2362ea5</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835036456-9 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Maheshwari A., Christ (Deemed to Be University), School of Commerce, Finance and Accountancy, Ghaziabad, India; Malhotra A., Christ (Deemed to Be University), School of Commerce, Finance and Accountancy, Ghaziabad, India; Singh Hada B., Christ (Deemed to Be University), School of Commerce, Finance and Accountancy, Delhi-NCR, India; Ranka M., Dayananda Sagar College of Arts, Science and Commerce, Bengaluru, India; Amzad Basha M.S., Gitam (Deemed to Be University), Gitam School of Business, Bengaluru, India