update Part_A.py and Part_B.py

Part_A.py

@@ -60,7 +60,7 @@ def main():
         ("Multi-layer Perceptron" , MLPClassifier(max_iter = 10000, solver = "adam", activation = "tanh", learning_rate = "constant").fit(train_features_scaled, train_labels)),
         ("C-Support Vector" , SVC(C = 7.989999999999979, kernel = "linear").fit(train_features_scaled, train_labels)),
 
-        # These algorithms were optimised and trained but were found to be overfitting
+        # These algorithms were optimised and can be trained, but were commented out as they were found to be overfitting
         #("Random Forest" , RandomForestClassifier(n_estimators = 418, max_depth = 5).fit(train_features_scaled, train_labels)),
         #("Decision Tree" , DecisionTreeClassifier(max_features = "sqrt", criterion = "gini", max_depth = 19).fit(train_features_scaled, train_labels)),
         #("K-Nearest Neighbours" , KNeighborsClassifier(n_neighbors = 4, n_jobs = -1, leaf_size = 68, metric = "manhattan", weights = "distance", algorithm = "kd_tree").fit(train_features_scaled, train_labels))

Part_B.py

@@ -56,9 +56,9 @@ def main():
 
     # Train the classifiers
     classifiers = [
-        #("Logistical Regression" , LogisticRegression(max_iter = 10000, solver = "newton-cg", C = 9.088000000000001).fit(train_features_scaled, train_labels)),
+        ("Logistical Regression" , LogisticRegression(max_iter = 10000, solver = "newton-cg", C = 9.088000000000001).fit(train_features_scaled, train_labels)),
         ("Multi-layer Perceptron" , MLPClassifier(max_iter = 10000, solver = "adam", activation = "tanh", learning_rate = "adaptive").fit(train_features_scaled, train_labels)),
-        #("C-Support Vector" , SVC(C = 9.59, kernel = "linear").fit(train_features_scaled, train_labels))
+        ("C-Support Vector" , SVC(C = 9.59, kernel = "linear").fit(train_features_scaled, train_labels))
     ]
 
     # Evaluate the performance of the trained classifiers