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Next, five ML classifiers were implemented using the libraries “sklearn.neighbors import KNeighborsClassifier” [(Rico-González et al., 2022b) for K-nearest neighbors classifier (KNN)], “from sklearn.ensemble import GradientBoostingClassifier” [(Kellmann et al., 2018) for Gradient Boosting Classifier (XGbosst)], “from sklearn.svm import SVC” [(Teixeira et al., 2022a) for support vector machine (SVM)], “from sklearn.ensemble import RandomForestClassifier” [(Teixeira et al., 2022b) for RF], and “from sklearn.tree import DecisionTreeClassifier” [(Teixeira et al., 2023) for DT Classifier] were activated to apply the algorithms and perform the recovery state classification (Python, 2023

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