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This repo explores feature selection and explainability in machine learning models that predict iteration success in Agile projects. We reduce features by 90% without loss in accuracy and highlight key drivers using state-of-the-art explainability tools.

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UCLM-SIMD/SDEE

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This repo explores feature selection and explainability in machine learning models that predict iteration success in Agile projects. We reduce features by 90% without loss in accuracy and highlight key drivers using state-of-the-art explainability tools.

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