diff --git a/machine_learning/multinomial_naive_bayes_classifier.py b/machine_learning/multinomial_naive_bayes_classifier.py
index d0bff15e6..ccbe6309e 100644
--- a/machine_learning/multinomial_naive_bayes_classifier.py
+++ b/machine_learning/multinomial_naive_bayes_classifier.py
@@ -11,12 +11,13 @@ https://en.wikipedia.org/wiki/Naive_Bayes_classifier
 import doctest
 
 import numpy as np
+import scipy
 from sklearn.datasets import fetch_20newsgroups
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics import accuracy_score
 
 
-def group_indices_by_target(targets):
+def group_indices_by_target(targets: np.ndarray) -> dict[int, list[int]]:
     """
     Associates to each target label the indices of the examples with that label
 
@@ -37,7 +38,7 @@ def group_indices_by_target(targets):
     >>> group_indices_by_target(y)
     {1: [0, 3], 2: [1, 4], 3: [2], 5: [5]}
     """
-    grouped_indices = {}
+    grouped_indices: dict[int, list[int]] = {}
     for i, y in enumerate(targets):
         if y not in grouped_indices:
             grouped_indices[y] = []
@@ -46,13 +47,13 @@ def group_indices_by_target(targets):
 
 
 class MultinomialNBClassifier:
-    def __init__(self, alpha=1):
-        self.classes = None
-        self.features_probs = None
-        self.priors = None
+    def __init__(self, alpha: int = 1) -> None:
+        self.classes: list[int] = []
+        self.features_probs: np.ndarray = np.array([])
+        self.priors: np.ndarray = np.array([])
         self.alpha = alpha
 
-    def fit(self, data, targets):
+    def fit(self, data: scipy.sparse.csr_matrix, targets: np.ndarray) -> None:
         """
         Parameters
         ----------
@@ -61,6 +62,17 @@ class MultinomialNBClassifier:
 
         targets : array-like of shape (n_samples,)
             Target labels
+
+        Example
+        ----------
+        >>> from scipy import sparse
+        >>> rng = np.random.RandomState(1)
+        >>> data = rng.randint(5, size=(6, 100))
+        >>> data = sparse.csr_matrix(data)
+        >>> y = np.array([1, 2, 3, 4, 5, 6])
+        >>> model = MultinomialNBClassifier()
+        >>> print(model.fit(data, y))
+        None
         """
         n_examples, n_features = data.shape
         grouped_indices = group_indices_by_target(targets)
@@ -79,7 +91,7 @@ class MultinomialNBClassifier:
                     tot_features_count + self.alpha * n_features
                 )
 
-    def predict(self, data):
+    def predict(self, data: scipy.sparse.csr_matrix) -> np.ndarray:
         """
         Parameters
         ----------
@@ -93,8 +105,6 @@ class MultinomialNBClassifier:
 
         Example
         ----------
-        Let's test the function following an example taken from the documentation
-        of the MultinomialNB model from sklearn
         >>> from scipy import sparse
         >>> rng = np.random.RandomState(1)
         >>> data = rng.randint(5, size=(6, 100))
@@ -118,7 +128,7 @@ class MultinomialNBClassifier:
         return np.array(y_pred)
 
 
-def main():
+if __name__ == "__main__":
     newsgroups_train = fetch_20newsgroups(subset="train")
     newsgroups_test = fetch_20newsgroups(subset="test")
     x_train = newsgroups_train["data"]
@@ -138,8 +148,4 @@ def main():
         "Accuracy of naive bayes text classifier: "
         + str(accuracy_score(y_test, y_pred))
     )
-
-
-if __name__ == "__main__":
-    main()
     doctest.testmod()