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 Predictive Machine Learning Approaches for Estimating Residential Rental Rates in India Subject The topic of the resource Decision Tree Algorithm; Gradient Boosting Technique; Linear Regression Model; Random Forest Algorithm; Rental Rate Description An account of the resource As urban areas like Chennai and Bangalore witness a continuous surge in land and housing prices, accurately estimating the market value of houses has become increasingly crucial. This presents a formidable challenge, prompting a growing demand for an accessible and efficient method to predict house rental prices, ensuring dependable forecasts for future generations. In response to this need, this study delves into the core factors influencing rental prices, with a keen focus on location and area. Leveraging a dataset comprising ten essential features tailored for detecting Rental Price in Metropolitan cities, the research meticulously preprocesses the data using a Python library to ensure data cleanliness, laying a robust foundation for constructing the predictive model. Employing a diverse range of Machine Learning algorithms, including Random Forest, Linear Regression, Decision Tree Regression, and Gradient Boosting, the study evaluates their efficacy in forecasting rental prices. Notably, feature extraction underscores the significance of area and property type in shaping rental prices. In comparison with existing methodologies, this research adopts gradient boosting as its preferred approach, achieving the most satisfactory predictive outcomes. Evaluation metrics are meticulously analyzed to validate the model's performance. Through this comprehensive analysis, the study not only offers valuable insights into rental price prediction but also ensures a rigorous comparison with existing approaches, maintaining originality and relevance in addressing the pressing challenges of housing market dynamics. 2024 IEEE. Creator An entity primarily responsible for making the resource Jayadharshini P.; Ragunath R.; Prabhakaran D.; Krishnasamy L.; Abinaya N.; Priyanka S. Source A related resource from which the described resource is derived TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024 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/TQCEBT59414.2024.10545253" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT59414.2024.10545253</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204457681&amp;doi=10.1109%2FTQCEBT59414.2024.10545253&amp;partnerID=40&amp;md5=e1120ea3fb4ecffa3d27417986fdc147" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204457681&amp;doi=10.1109%2fTQCEBT59414.2024.10545253&amp;partnerID=40&amp;md5=e1120ea3fb4ecffa3d27417986fdc147</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038427-7 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 Jayadharshini P., Kongu Engineering College, Department of Artificial Intelligence, Tamilnadu, Erode, India; Ragunath R., Nandha Engineering College, Department of Information Technology, Tamilnadu, Erode, India; Prabhakaran D., Nandha Engineering College, Department of Information Technology, Tamilnadu, Erode, India; Krishnasamy L., Christ University, Department of Cse, Bangalore, India; Abinaya N., Kongu Engineering College, Department of Artificial Intelligence, Erode, India; Priyanka S., Kongu Engineering College, Department of Artificial Intelligence, Tamilnadu, Erode, India