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 An Efficient Machine Learning Approach: Analysis of Supervised Machine Learning Methods to Forecast the Diamond Price Subject The topic of the resource Diamonds; Estimated values; Machine learning; Regression Models and Cross validation Description An account of the resource Diamond, a found natural process compound of carbon, is one of the hardest and most immensely expensive material known to men, especially more to women. Investments in expensive gems like diamonds are in significant demand. The rate of a diamond, nevertheless, is not as easily calculated as the value of either gold or platinum since so many factors must be taken into account. Because there is such a broad range of diamond dimensions and qualities; as a result, being able to make reliable price predictions is crucial for the diamond industry. Although, making accurate predictions is challenging. In this study, we implemented multiple machine learning techniques employed to the challenge of diamond price forecasting's such as Linear Regression, Random Forest, Decision Tree Random Forest, Cat-Boost Regressor and XGB Regressor. This article's goal is to develop an accurate model for estimating diamond prices based on its characteristics such as weighting factor, cut grade, and dimensions. We compared the sum of estimated values and test values of predicted values with overestimated, underestimated and exact estimations. We applied cross-validation to calculate how much the model deviates from the actual when faced with a difference between the training set and the test set. We predicted values side by side. We performed a comparative analysis of supervised machine learning models with other models to evaluate the model accuracy and performance metrics. The Study's experimental findings show that out of all the supervised machine learning models, Random Forest performs well with R2score and Low RMSE and MAE values and CV Score. 2023 IEEE. Creator An entity primarily responsible for making the resource Basha M.S.A.; Oveis P.M.; Prabavathi C.; Lakshmi M.B.; Sucharitha M.M. Source A related resource from which the described resource is derived 2023 International Conference for Advancement in Technology, ICONAT 2023 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 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICONAT57137.2023.10080618" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICONAT57137.2023.10080618</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153282231&amp;doi=10.1109%2FICONAT57137.2023.10080618&amp;partnerID=40&amp;md5=1b4cedfa4958cc1932e36421c929fccb" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153282231&amp;doi=10.1109%2fICONAT57137.2023.10080618&amp;partnerID=40&amp;md5=1b4cedfa4958cc1932e36421c929fccb</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166547517-4 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 Basha M.S.A., GITAM School of Business, Gandhi Institute of Technology and Management, Deemed to Be University, Bengaluru, India; Oveis P.M., GITAM School of Business, Gandhi Institute of Technology and Management, Deemed to Be University, Bengaluru, India; Prabavathi C., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Lakshmi M.B., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Sucharitha M.M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India