Update PartB.py

PartB.py

 import pandas as pd
 from sklearn.model_selection import train_test_split
 from sklearn.linear_model import LogisticRegression
-from sklearn.metrics import accuracy_score
+from sklearn.metrics import accuracy_score, f1_score
 
 # Load the training data and testing data into a Pandas DataFrame
 data = pd.read_csv('TrainingDataMulti.csv', header=None)
@@ -14,6 +14,7 @@ y = data.iloc[:, -1]
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)
 
 
+def Logistic_Regression():
     # Fit data using Logistic Regression model training
     LR = LogisticRegression(C=1e5, max_iter=5000)
     LR.fit(X_train, y_train)
@@ -21,11 +22,16 @@ LR.fit(X_train, y_train)
     # Make predictions on the testing data
     y_pred = LR.predict(X_test)
 
-# Evaluate the accuracy of the model
+    # Testing the accuracy of the model
     accuracy = accuracy_score(y_test, y_pred)
+    # Testing the F1 score of the model
+    f1 = f1_score(y_test, y_pred, average='weighted')
+    print("+-------------------------------------------+")
     print(f"LogisticRegression Accuracy: {accuracy}")
+    print(f"LogisticRegression F1 Score: {f1}")
 
 
+def Decision_Tree():
     # Fit data using Decision Tree model training
     from sklearn.tree import DecisionTreeClassifier
     DT = DecisionTreeClassifier(random_state=0)
@@ -36,36 +42,58 @@ y_pred = DT.predict(X_test)
 
     # Evaluate the accuracy of the model
     accuracy = accuracy_score(y_test, y_pred)
+    # Testing the F1 score of the model
+    f1 = f1_score(y_test, y_pred, average='weighted')
+    print("+-------------------------------------------+")
     print(f"DecisionTree Accuracy: {accuracy}")
+    print(f"DecisionTree F1 Score: {f1}")
 
 
+def Random_Forest():
     # Fit data using Random Forest model training
     from sklearn.ensemble import RandomForestClassifier
     RF = RandomForestClassifier(random_state=0)
     RF.fit(X_train, y_train)
     y_pred = RF.predict(X_test)
     accuracy = accuracy_score(y_test, y_pred)
+    f1 = f1_score(y_test, y_pred, average='weighted')
+    print("+-------------------------------------------+")
     print(f"RandomForest Accuracy: {accuracy}")
+    print(f"RandomForest F1 Score: {f1}")
+    return RF
 
 
+def svm():
     # Fit data using SVM model training
     from sklearn.svm import SVC
     SVM = SVC(random_state=0)
     SVM.fit(X_train, y_train)
     y_pred = SVM.predict(X_test)
     accuracy = accuracy_score(y_test, y_pred)
+    f1 = f1_score(y_test, y_pred, average='weighted')
+    print("+-------------------------------------------+")
     print(f"SVM Accuracy: {accuracy}")
+    print(f"SVM F1 Score: {f1}")
 
 
+def knn():
     # Fit data using KNN model training
     from sklearn.neighbors import KNeighborsClassifier
     KNN = KNeighborsClassifier()
     KNN.fit(X_train, y_train)
     y_pred = KNN.predict(X_test)
     accuracy = accuracy_score(y_test, y_pred)
+    f1 = f1_score(y_test, y_pred, average='weighted')
+    print("+-------------------------------------------+")
     print(f"KNN Accuracy: {accuracy}")
-
-
+    print(f"KNN F1 Score: {f1}")
+
+if __name__ == "__main__":
+    Logistic_Regression()
+    Decision_Tree()
+    RF = Random_Forest()
+    svm()
+    knn()
     # Use Random Forest model to predict the test data and save the results to a CSV file
     pridictions = RF.predict(test_data)
     test_data['Prediction'] = pridictions