diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..793a6b2e 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -40,8 +40,13 @@ def max_index(X):
     i = 0
     j = 0
 
-    # TODO
-
+    if not isinstance(X, np.ndarray) or X.ndim != 2:
+        raise ValueError("X must be a 2D numpy array")
+    else:
+        for n in range(X.shape[0]):
+            for m in range(X.shape[1]):
+                if X[n, m] > X[i, j]:
+                    i, j = n, m
     return i, j
 
 
@@ -64,4 +69,11 @@ def wallis_product(n_terms):
     """
     # 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:
+        wallis = 1
+    else:
+        wallis = 1
+        for n in range(n_terms):
+            wallis *= (4 * (n+1)**2)/((4 * (n+1)**2) - 1)
+        wallis *= 2
+    return wallis
diff --git a/sklearn_questions.py b/sklearn_questions.py
index 948c2e54..32676b12 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -30,7 +30,7 @@
 
 
 class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
@@ -45,7 +45,8 @@ def fit(self, X, 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):
@@ -55,12 +56,27 @@ def predict(self, X):
         """
         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 {self.__class__.__name__} "
+                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 in range(len(X)):
+            min_dist = None
+            index_min = None
+            x = X[i]
+            for j in range(len(self.X_train_)):
+                x_train = self.X_train_[j]
+                dist = np.linalg.norm(x - x_train)
+                if min_dist is None or dist < min_dist:
+                    min_dist = dist
+                    index_min = j
+            y_pred[i] = self.y_train_[index_min]
         return y_pred
 
     def score(self, X, y):
@@ -70,6 +86,6 @@ def score(self, X, y):
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
+        accuracy = sum(y_pred == y) / len(y)
 
-        # XXX fix
-        return y_pred.sum()
+        return accuracy