diff --git a/rr/experiment/algorithm.py b/rr/experiment/algorithm.py
index b6759eb..8173a02 100644
--- a/rr/experiment/algorithm.py
+++ b/rr/experiment/algorithm.py
@@ -9,14 +9,14 @@ class Model:
     def __init__(self, nb_tree_per_forest=50, max_depth=10):
         """Create a new ML model (Random forest classifier from scikitlearn)
 
-    Args:
-        nb_tree_per_forest (int): number of decision trees in the forest
-        max_depth (int): max depth of the trees
-    Returns:
-        None
-    Raises:
-        None
-    """
+        Args:
+            nb_tree_per_forest (int): number of decision trees in the forest
+            max_depth (int): max depth of the trees
+        Returns:
+            None
+        Raises:
+            None
+        """
         self.model = RandomForestClassifier(
             n_estimators=nb_tree_per_forest, max_depth=max_depth, random_state=0
         )
@@ -24,28 +24,28 @@ def __init__(self, nb_tree_per_forest=50, max_depth=10):
     def train(self, X, y):
         """Train the model using the given data
 
-    Args:
-        X (numpy.ndarray):A NxM 2D-array where each row corresponds to a sample and each column to a feature
-        y (numpy.ndarray): A 1D-array of length N, where each element corresponds to a sample label
-    Returns:
-        None
-    Raises:
-        None
-    """
+        Args:
+            X (numpy.ndarray):A NxM 2D-array where each row corresponds to a sample and each column to a feature
+            y (numpy.ndarray): A 1D-array of length N, where each element corresponds to a sample label
+        Returns:
+            None
+        Raises:
+            None
+        """
         self.model.fit(X, y)
 
     def predict(self, X):
         """Make a prediction on the data using the trained model
 
-    Args:
-        X (numpy.ndarray):A NxM 2D-array where each row corresponds to a sample and each column to a feature
-    Returns:
-        numpy.ndarray: A 1D array (with a dtype of int) containing the predicted
-        label for each sample
-                       
-    Raises:
-        None
-    """
+        Args:
+            X (numpy.ndarray):A NxM 2D-array where each row corresponds to a sample and each column to a feature
+        Returns:
+            numpy.ndarray: A 1D array (with a dtype of int) containing the predicted
+            label for each sample
+
+        Raises:
+            None
+        """
         prediction = self.model.predict(X)
 
         return prediction
diff --git a/rr/experiment/analysis.py b/rr/experiment/analysis.py
index e1d6172..d42abe7 100644
--- a/rr/experiment/analysis.py
+++ b/rr/experiment/analysis.py
@@ -8,7 +8,7 @@ def get_confusion_matrix(prediction_label, true_label):
 
     Args:
         prediction_label (list): Estimated targets as returned by a classifier.
-        true_label (list): Ground truth (correct) target values.   
+        true_label (list): Ground truth (correct) target values.
     Returns:
         numpy.ndarray: A 2D array (with a dtype of int) containing the confusion matrix.
     Raises:
@@ -25,7 +25,7 @@ def plot_confusion_matrix(
     Args:
         cm (numpy.ndarray): A 2D array (with a dtype of int) containing the confusion matrix.
         classes (numpy.ndarray): A 1D array (with a dtype of str) containing the lable name for each class
-        normalize (boolean): Flag to normalize the data 
+        normalize (boolean): Flag to normalize the data
         title (str): The title of the plot
         file_name (str): File name to export the graph
     Returns:
diff --git a/rr/experiment/experiments.py b/rr/experiment/experiments.py
index 0b1caaf..c9aeb68 100644
--- a/rr/experiment/experiments.py
+++ b/rr/experiment/experiments.py
@@ -69,7 +69,9 @@ def experiment_impact_nb_trees(tabnum, filepath, nb_trees, max_depth, plot_path)
         for m, nb_tree_per_forest in enumerate(nb_trees):
             num = (n * len(nb_trees)) + m + tabnum
             result += "\nTable {table_number}: Confusion matrix with {nb_trees} tree(s) for Protocol `{protocol}`".format(
-                table_number=num, protocol=p, nb_trees=nb_tree_per_forest,
+                table_number=num,
+                protocol=p,
+                nb_trees=nb_tree_per_forest,
             )
             cm = base_experiment(
                 p,
@@ -109,7 +111,9 @@ def experiment_impact_tree_depth(tabnum, filepath, nb_trees, max_depths, plot_pa
         for m, max_depth in enumerate(max_depths):
             num = (n * len(max_depths)) + m + tabnum
             result += "\nTable {table_number}: Confusion matrix with trees maximum depth of {max_depth} for Protocol `{protocol}`".format(
-                table_number=num, protocol=p, max_depth=max_depth,
+                table_number=num,
+                protocol=p,
+                max_depth=max_depth,
             )
             cm = base_experiment(
                 p,