diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..3c9611e6 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -40,7 +40,13 @@ def max_index(X):
     i = 0
     j = 0
 
-    # TODO
+    if not isinstance(X, np.ndarray):
+        raise ValueError("Input must be a numpy array")
+    if X.ndim != 2:
+        raise ValueError("Input array must be 2-dimensional")
+
+    flat_index = np.argmax(X)
+    i, j = np.unravel_index(flat_index, X.shape)
 
     return i, j
 
@@ -60,8 +66,12 @@ def wallis_product(n_terms):
     Returns
     -------
     pi : float
-        The approximation of order `n_terms` of pi using the Wallis product.
+    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
+
+    product = 1.0
+    for n in range(1, n_terms+1):
+        product *= (4 * n**2) / (4 * n**2 - 1)
+    return 2 * product
diff --git a/sklearn_questions.py b/sklearn_questions.py
index f65038c6..450758de 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -28,29 +28,37 @@
 from sklearn.utils.multiclass import check_classification_targets
 
 
-class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
+        """Fit the 1-NN classifier from the training data.
 
-        And describe parameters
+        X : ndarray of shape (n_samples, n_features)
+        y : ndarray of shape (n_samples,)
+
+        Returns 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_train_ = X
+        self.y_train_ = y
+
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Predict class labels for the provided data.
 
-        And describe parameters
+        X : ndarray of shape (n_samples, n_features)
+
+        Return :
+        y_pred : ndarray of shape (n_samples,) -> the predicted class labels.
         """
         check_is_fitted(self)
         X = check_array(X)
@@ -59,16 +67,21 @@ def predict(self, X):
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        for i, x in enumerate(X):
+            distances = np.linalg.norm(self.X_train_ - x, axis=1)
+            nearest_index = np.argmin(distances)
+            y_pred[i] = self.y_train_[nearest_index]
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
+        """Return the mean accuracy on the given test data and labels.
+
+        X : ndarray of shape (n_samples, n_features)
+        y : ndarray of shape (n_samples,)
 
-        And describe parameters
+        Return the score
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)