diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..3d82794c 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -37,12 +37,15 @@ def max_index(X):
         If the input is not a numpy array or
         if the shape is not 2D.
     """
-    i = 0
-    j = 0
+    if not isinstance(X, np.ndarray):
+        raise ValueError("L'entrée doit être un tableau numpy")
 
-    # TODO
+    if X.ndim != 2:
+        raise ValueError("Le tableau doit être de dimension 2")
 
-    return i, j
+    i, j = np.unravel_index(np.argmax(X), X.shape)
+
+    return int(i), int(j)
 
 
 def wallis_product(n_terms):
@@ -62,6 +65,12 @@ def wallis_product(n_terms):
     pi : float
         The approximation of order `n_terms` of pi using the Wallis product.
     """
-    # XXX : The n_terms is an int that corresponds to the number of
-    # terms in the product. For example 10000.
-    return 0.
+    if n_terms == 0:
+        return 1.0
+
+    n = np.arange(1, n_terms + 1, dtype=float)
+
+    # Formule mathématique simplifiée du terme n : (4n^2) / (4n^2 - 1)
+    produit = np.prod((4 * n**2) / (4 * n**2 - 1))
+
+    return float(2.0 * produit)
diff --git a/sklearn_questions.py b/sklearn_questions.py
index 948c2e54..4f09c51c 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -25,51 +25,88 @@
 from sklearn.utils.validation import check_X_y
 from sklearn.utils.validation import check_array
 from sklearn.utils.validation import check_is_fitted
-from sklearn.utils.validation import validate_data
 from sklearn.utils.multiclass import check_classification_targets
 
 
 class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Fit the OneNearestNeighbor model from the training dataset.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            contains the X features of the training set
+        y : ndarray of shape (n_samples,)
+            contains the target values of each line
+
+        Returns
+        -------
+        self : OneNearestNeighbor
+            The fitted estimator
         """
         X, y = check_X_y(X, y)
         check_classification_targets(y)
         self.classes_ = np.unique(y)
         self.n_features_in_ = X.shape[1]
 
-        # XXX fix
+        self.X_ = X
+        self.y_ = y
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Predict the value of y for the X sample.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The input samples.
 
-        And describe parameters
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Predicted value of y for the X sample.
         """
         check_is_fitted(self)
         X = check_array(X)
+        if X.shape[1] != self.n_features_in_:
+            raise ValueError(
+                f"X has {X.shape[1]} features, but OneNearestNeighbor "
+                f"is expecting {self.n_features_in_} features as input."
+            )
         y_pred = np.full(
             shape=len(X), fill_value=self.classes_[0],
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        for i, x_i in enumerate(X):
+            distance = np.linalg.norm(self.X_ - x_i, axis=1)
+            min_distance_ind = np.argmin(distance)
+            y_pred[i] = self.y_[min_distance_ind]
+
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
+        """Give a score to our model.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Contains the X features of the test set.
+
+        y : ndarray of shape (n_samples,)
+            Contains the target value we want to reach.
 
-        And describe parameters
+        Returns
+        -------
+        score : float
+            Score of the model.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        return float(np.mean(y_pred == y))