Algorithm 1. Stacked Ensemble for Prediction

Step 1: Split the data set  • X = The attributes  • Y = The Status Step 2: Balance the dataset using SMOTE Step 3: Split the Dataset into training and testing set • x_train, x_test, y_train, y_test, stratify y and test_size = 0.3 Step 4: Import stacking classifier from Sklearn Library Step 5: Import all the classifiers also from Sklearn Library  • Import SVM  • Import K-neighbor classifier  • Import LR  • Import RF Classifier Step 6: Initiate the hyper parameters of the Classifiers Step 7: Stacked the classifiers and initiate LR as the final estimator Step 8: Train the Stacked Ensemble with x_train and y_train Step 9: Predict using x_test Step 10: Print the Confusion matrix, Classification report and accuracy score. Step 11: End Abbreviations—SMOTE: Synthetic Minority Oversampling Technique; SVM: Support Vector Machine; LR: Logistic Regression; RF: Random Forest