diff --git a/machine_learning/gaussian_naive_bayes.py.broken.txt b/machine_learning/gaussian_naive_bayes.py.broken.txt
index 7e9a8d7f6dcf..6be882854192 100644
--- a/machine_learning/gaussian_naive_bayes.py.broken.txt
+++ b/machine_learning/gaussian_naive_bayes.py.broken.txt
@@ -1,18 +1,24 @@
 # Gaussian Naive Bayes Example
 import time
-
 from matplotlib import pyplot as plt
 from sklearn.datasets import load_iris
 from sklearn.metrics import accuracy_score, plot_confusion_matrix
 from sklearn.model_selection import train_test_split
 from sklearn.naive_bayes import GaussianNB
 
-
 def main():
-
     """
     Gaussian Naive Bayes Example using sklearn function.
     Iris type dataset is used to demonstrate algorithm.
+
+    Algorithm:
+    1. Load Dataset: Import the Iris dataset.
+    2. Data Preprocessing: Split dataset into features (X) and labels (y).
+    3. Model Initialization: Initialize Gaussian Naive Bayes model.
+    4. Model Training: Fit model using training data.
+    5. Make Predictions: Predict labels for the test dataset.
+    6. Evaluate Model: Calculate and display accuracy.
+    7. Visualize Results: Generate and display a normalized confusion matrix.
     """
 
     # Load Iris dataset
@@ -37,7 +43,7 @@ def main():
         x_test,
         y_test,
         display_labels=iris["target_names"],
-        cmap="Blues",  # although, Greys_r has a better contrast...
+        cmap="Blues",  # although Greys_r has better contrast...
         normalize="true",
     )
     plt.title("Normalized Confusion Matrix - IRIS Dataset")
@@ -47,6 +53,5 @@ def main():
     final_accuracy = 100 * accuracy_score(y_true=y_test, y_pred=y_pred)
     print(f"The overall accuracy of the model is: {round(final_accuracy, 2)}%")
 
-
 if __name__ == "__main__":
     main()