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diagnostic-model

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This project focuses on analyzing and comparing the performance of SVM, Random Forest, and XGBoost in diagnosing depression based on individual data. The evaluation process incorporates Non-Parametric Statistical Testing, Feature Engineering, Resampling, and Hyperparameter Tuning. The project received a grade of 97/100 in Computational Intelligence

  • Updated Dec 11, 2024

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