Algorithm 3 main

Input: DataSpectre.xlsx Output: Score, Modelchoice Begin Reading data:   data = Read Excel file: “Data.xlsx” Extracting features and labels: X = Feature selection without the target variable: “Class” y = Reading data from the target variable: “Class” Splitting data into training and testing sets: X_train, X_test, y_train, y_test = Training and testing data split (X, y) Creating classifiers and models: Classifiers= [‘SVM’, ’Logistic’, ’RandomForest’, ’AdaBoost’, ’DecisionTree’, ’KNeighbors’, ‘XGBoost’] models=[SVM(), LogisticRegression(), RandomForestClassifier(), AdaBoostClassifier(), DecisionTreeClassifier(), KNeighborsClassifier(), XGBClassifier()] Writing a function to train, test, and evaluate models: Score, Modelchoice = Train_test_evaluate(X_train, X_test, y_train, y_test, models)                 G, S = Genetic(X, y, X_train, X_test, y_train, y_test, Modelchoice)                 Choose the indices associated with the highest scores in the variable ‘ind.’                 For vi in ind for i, value in enumerate(G[vi])         if value is True then                 append i to indices_true1                         end end X_train_GA = X_train with columns selected using indices in indices_true1 X_test_GA = X_test with columns selected using indices in indices_true1 Score, Modelchoice = Train_test_evaluate(X_train_GA, X_test_GA, y_train, y_test, models)                 end