diff --git a/breast_cancer_queries.py b/breast_cancer_queries.py
index 79372f8638751c546916fd7d7be5d63451710bb8..5c67bae6e3346a119f494d6b7582e638c394878a 100644
--- a/breast_cancer_queries.py
+++ b/breast_cancer_queries.py
@@ -4,10 +4,12 @@ import csv
 import sys
 from hle import high_level_single # our code
 
+# Load dataset from CSV file
 with open('data/breast-cancer.csv', 'r') as f:
     reader = csv.reader(f, delimiter=';')
     full_dataset = list(reader)
 
+# Define feature names
 features = {
     'clumpThickness': 'numeric',
     'uniformityCellSize': 'numeric',
@@ -20,16 +22,18 @@ features = {
     'mitoses': 'numeric',
 }
 
+# Define Class Names
 class_names = ['benign', 'melignant']
 
 feature_names = list(features.keys())
 feature_types = list(features.values())
 
-# because of binary features with values that are not 0 or 1.
+# because of binary features with values that are not 0 or 1. (not needed here, leaving it just in case we need it)
 feature_mapping = {
 
 }
 
+# Process row of features from dataset
 def process_features_student(row):
     to_delete = [0]
     cpy = []
@@ -43,21 +47,26 @@ def process_features_student(row):
     assert len(cpy) == len(feature_names)
     return cpy
 
+
+# Process Class Label
 def process_class(val):
     if float(val) >= 3: # good grade is a grade in  [10, 20]. Bad grade is [0, 10)
         return 0
     else:
         return 1
 
+# Prepare dataset by splitting features and labels
 dataset = full_dataset[1:]
 X = [ process_features_student(data[:-1]) for data in dataset]
 y = [ process_class(data[-1]) for data in dataset]
 
+# Init and Train decision tree classifier
 cancer_clf = DecisionTreeClassifier(max_leaf_nodes=400, random_state=0)
 cancer_clf.fit(X, y)
 
 print('DecisionTreeClassifier has been trained')
 
+# Example Queries (feel free to add more)
 q1 = 'exists p1, exists p2, benign(p1) implies benign(p2)'
 q2 = 'exists p1, exists p2, p1.blandChromatin > 3 and p2.marginalAdhesion <= 3 and melignant(p1) implies benign(p2)'
 q3 = 'for every patient, patient.blandChromatin > 4 implies melignant(patient)'
@@ -70,7 +79,7 @@ q6 = ('exists p1, exists p2, p1.mitoses <= 2 implies melignant(p1)'
       'and p2.blandChromatin > 9 implies p1.blandChromatin <= 3')
 
 
-
+# Eval Example Queries
 def example_queries():
     queries = [q1,q2,q3,q4,q5,q6]
     avg = 0