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 Yadav, Mahi; Manish, Maitreya; Jain, Yash; Srivastava, Roli; Singhal, Prateek Title A name given to the resource Hybrid Machine Learning Models for Crowdfunding Success Prediction Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1855 LNNS;pp.102-112 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-18910-3_9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-18910-3_9</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105040511840?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105040511840?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 Yadav M., School of Commerce, Christ (Deemed to be University), Delhi, India; Manish M., School of Commerce, Christ (Deemed to be University), Delhi, India; Jain Y., School of Commerce, Christ (Deemed to be University), Delhi, India; Srivastava R., School of Commerce, Christ (Deemed to be University), Delhi, India; Singhal P., Department Computer Science, School of Science, Christ (Deemed to be University), Delhi, India Description An account of the resource Crowdfunding has become a viable option for founders and entrepreneurs as an alternative source of funding, where individuals can access a large pool of supporters to fill the funding gap. There are a variety of reasons why it is difficult to predict if crowdfunding project will be successful. As such, types of projects, duration of the campaign, target funding goal, and overall supporter activity are constantly changing. This research, therefore, aims to explore, the use of machine learning for predictive models that quantitatively leverage the historical records of projects on kickstarter, in order to find the success probability. In order to analyze the predictive ability for campaign success, was used a combination of machine learning models - Logistic Regression, Random Forest, XGBoost, LightGBM, and Decision Trees - the models that had the greatest precision were Decision Tree (99.91% acc), and LightGBM (99.90% acc) hence why they were selected. In addition, this research demonstrates how feature selection coupled with ensemble learning can significantly increase predictive potential by providing valuable information for campaign builders, platform operators, and investors who are undertaking crowdfunding projects. These findings indicate that predictive modelling can support campaign design, promote investor trust, and enhance credibility for crowdfunding platforms, through uncovering fraud. Additional measures measuring social media interaction or sentiment analysis could be incorporated to provide information for better predictive models. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Crowdfunding; Decision Tree; Fundraising Success; LightGBM; Logistic Regression; Machine learning; Random Forest; XGBoost Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303218909-7; 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