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A two-step evaluation and validation process secured validity, while applying linear optimization methodology, considering constraints such as salary and player position to recommend an eight-player line-up for Daily Fantasy Sports (DFS). Four scenarios with 14 machine learning models and meta-models with a blending approach with an ensembling methodology were evaluated. Using individual per-player modeling, standard and advanced features, and different timespans resulted in accurate, well-established, and well-generalized predictions. Standard features improved MAPE results by 1.7\u20131.9% in the evaluation and 0.2\u20132.1% in the validation set. Additionally, two model selection cases were developed, with average scoring MAPEs of 28.90% and 29.50% and MAEs of 7.33 and 7.74 for validation sets. The most effective models included Voting Meta-Model, Random Forest, Bayesian Ridge, AdaBoost, and Elastic Net. 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