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This study aims to enhance the prediction of task in-progress time by integrating traditional features, such as developers\u2019 experience and task estimates, with affective states and personality traits. This article reports a long-term empirical study across seven agile projects in four software development companies, applying various machine learning algorithms to assess the predictive power of these combined features, evaluating them primarily through validation accuracy score. Also, we investigated the impact of weighting developers\u2019 affective states based on their personality traits on model performance. Incorporating developers\u2019 affective states and personality traits improved the task in-progress time prediction in 42.59% of classifier, dataset, and scaling combinations, with oversampled combinations achieving up to 8.4% higher validation accuracy than traditional feature models. The innovative weighting strategy improved 31.48% of the combinations. 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