Update gaussian_naive_bayes.py

machine_learning/gaussian_naive_bayes.py

@@ -1,11 +1,15 @@
 # Gaussian Naive Bayes Example
+import time
+
+import seaborn as sns  # For plotting a heatmap of the confusion matrix. This way will not throw any warning.
 from matplotlib import pyplot as plt
 from sklearn.datasets import load_iris
-from sklearn.metrics import accuracy_score, confusion_matrix # The plot_confusion_matrix method will be deprecated in the developed versions of Python 3.10.x according to the warning that it throws when this code is run.
+from sklearn.metrics import (  # The plot_confusion_matrix method will be deprecated in the developed versions of Python 3.10.x according to the warning that it throws when this code is run.
+    accuracy_score,
+    confusion_matrix,
+)
 from sklearn.model_selection import train_test_split
 from sklearn.naive_bayes import GaussianNB
-import time
-import seaborn as sns # For plotting a heatmap of the confusion matrix. This way will not throw any warning.
 
 
 def main():
@@ -31,17 +35,18 @@ def main():
     y_pred = model_fit.predict(x_test)
     # Display Confusion Matrix
     Conf_Matrix = confusion_matrix(y_true=y_test, y_pred=y_pred)
-    sns.heatmap(data = Conf_Matrix, annot = True, cmap = "Greys_r")
+    sns.heatmap(data=Conf_Matrix, annot=True, cmap="Greys_r")
     plt.show()
-    
+
     # Printing the seen confusion matrix on the console
     time.sleep(1.2)
     print("The confusion matrix is:\n", Conf_Matrix)
-    
+
     time.sleep(1.8)
     # Declaring the overall accuracy of the model
-    final_accuracy = 100*accuracy_score(y_true = y_test, y_pred = y_pred)
+    final_accuracy = 100 * accuracy_score(y_true=y_test, y_pred=y_pred)
     print(f"The final accuracy of the model is: {round(final_accuracy, 2)}%")
-
+
+
 if __name__ == "__main__":
     main()