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It is mainly used to link projects\u2019 creators and backers, collect money and plan fundraising projects via social networks. This paper proposes a machine learning-enabled approach to analyse Kickstarter numerical and textual data and predict the successful funding and delivery of crowdfunding projects. It offers crowdfunding stakeholders benefits including creator credibility assessment, project risk reduction, and backer confidence enhancement. This research proposes a data preprocessing approach to prepare the dataset and extract the relevant features for the predictions. Besides, it trains and compares five numerical machine learning classification models and three text-mining methods to find the best-fitted numerical and textual analysis approaches. According to the results, the proposed SVM model outperforms the numerical benchmarks in terms of Accuracy, Precision, Recall, F1 score, and model Training latency. 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