diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..4845bc06 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -15,6 +15,7 @@
 This will be enforced with `flake8`. You can check that there is no flake8
 errors by calling `flake8` at the root of the repo.
 """
+
 import numpy as np
 
 
@@ -37,12 +38,16 @@ 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("Input should be numpy array.")
+    if X.ndim != 2:
+        raise ValueError("Input should be 2D array.")
 
     # TODO
+    max_val = X.max()
+    i, j = np.where(X == max_val)
 
-    return i, j
+    return i[0], j[0]
 
 
 def wallis_product(n_terms):
@@ -64,4 +69,12 @@ 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.
+    pi_approx = 1
+
+    if n_terms == 0:
+        return pi_approx
+
+    for n in range(1, n_terms + 1):
+        pi_approx *= (4 * n**2) / (4 * n**2 - 1)
+
+    return 2 * pi_approx
diff --git a/sklearn_questions.py b/sklearn_questions.py
index f65038c6..53b9efce 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -29,7 +29,7 @@
 
 
 class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
@@ -41,10 +41,11 @@ def fit(self, X, y):
         """
         X, y = check_X_y(X, y)
         check_classification_targets(y)
+        self.X_ = X
+        self.y_ = y
         self.classes_ = np.unique(y)
         self.n_features_in_ = X.shape[1]
 
-        # XXX fix
         return self
 
     def predict(self, X):
@@ -54,12 +55,14 @@ def predict(self, X):
         """
         check_is_fitted(self)
         X = check_array(X)
-        y_pred = np.full(
-            shape=len(X), fill_value=self.classes_[0],
-            dtype=self.classes_.dtype
+
+        # euclidean distances between X and self.X_
+        distances = np.linalg.norm(
+            self.X_[np.newaxis, :, :] - X[:, np.newaxis, :], axis=2
         )
+        nearest_indices = np.argmin(distances, axis=1)
+        y_pred = self.y_[nearest_indices]
 
-        # XXX fix
         return y_pred
 
     def score(self, X, y):
@@ -70,5 +73,4 @@ def score(self, X, y):
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)