update Part_A.py and Part_B.py

Part_A.py

 # Import libraries and modules
 import pandas as pd
 import numpy as np
+import matplotlib.pyplot as plt
 from skopt import BayesSearchCV
 from skopt.space import Real, Categorical, Integer
 from sklearn.model_selection import cross_val_score
 from sklearn.utils import shuffle
 from sklearn.preprocessing import StandardScaler
-from sklearn.metrics import accuracy_score, f1_score
+from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, ConfusionMatrixDisplay
 from sklearn.linear_model import LogisticRegression
 from sklearn.neural_network import MLPClassifier
 from sklearn.svm import SVC
@@ -56,7 +57,7 @@ def main():
 
     # Train the classifiers
     classifiers = [
-        ("Logistical Regression" , LogisticRegression(max_iter = 10000, solver = "newton-cg", C = 9.416).fit(train_features_scaled, train_labels)),
+        ("Logistic Regression" , LogisticRegression(max_iter = 10000, solver = "newton-cg", C = 9.416).fit(train_features_scaled, train_labels)),
         ("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)),
 
@@ -92,9 +93,14 @@ def main():
             best_classifier_name = classifier_name
             best_classifier = classifier
             best_classifier_average = cv_average
+            best_classifier_predictions = train_predicted_labels
 
     print(f"The best classifier is {best_classifier_name}.\n")
 
+    # Draw confusion matrix for the best classifier
+    ConfusionMatrixDisplay(confusion_matrix = confusion_matrix(train_labels, best_classifier_predictions)).plot()
+    plt.show()
+
     # Read the training data
     test_features = pd.read_csv("TestingDataBinary.csv", header = None)
 

Part_B.py

 # Import libraries and modules
 import pandas as pd
 import numpy as np
+import matplotlib.pyplot as plt
 from skopt import BayesSearchCV
 from skopt.space import Real, Categorical, Integer
 from sklearn.model_selection import cross_val_score
 from sklearn.utils import shuffle
 from sklearn.preprocessing import StandardScaler
-from sklearn.metrics import accuracy_score, f1_score
+from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, ConfusionMatrixDisplay
 from sklearn.linear_model import LogisticRegression
 from sklearn.neural_network import MLPClassifier
 from sklearn.svm import SVC
@@ -56,7 +57,7 @@ def main():
 
     # Train the classifiers
     classifiers = [
-        ("Logistical Regression" , LogisticRegression(max_iter = 10000, solver = "newton-cg", C = 9.088000000000001).fit(train_features_scaled, train_labels)),
+        ("Logistic 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))
     ]
@@ -87,15 +88,14 @@ def main():
             best_classifier_name = classifier_name
             best_classifier = classifier
             best_classifier_average = cv_average
-
-        # Update the best classifier if the current classifier has a better average score
-        if (best_classifier_average < cv_average):
-            best_classifier_name = classifier_name
-            best_classifier = classifier
-            best_classifier_average = cv_average
+            best_classifier_predictions = train_predicted_labels
 
     print(f"The best classifier is {best_classifier_name}.\n")
 
+    # Draw confusion matrix for the best classifier
+    ConfusionMatrixDisplay(confusion_matrix = confusion_matrix(train_labels, best_classifier_predictions)).plot()
+    plt.show()
+
     # Read the training data
     test_features = pd.read_csv("TestingDataMulti.csv", header = None)