diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..d2037499 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -37,12 +37,23 @@ 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("X must be a numpy array")
+    if X.ndim != 2:
+        raise ValueError("X must be a 2D numpy array")
 
-    # TODO
+    i_max = 0
+    j_max = 0
+    max_value = X[0, 0]
 
-    return i, j
+    for i in range(X.shape[0]):
+        for j in range(X.shape[1]):
+            if X[i, j] >= max_value:
+                max_value = X[i, j]
+                i_max = i
+                j_max = j
+
+    return i_max, j_max
 
 
 def wallis_product(n_terms):
@@ -62,6 +73,11 @@ 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
+    product = 1
+    for i in range(1, n_terms + 1):
+        numerator = 4 * i * i
+        denominator = numerator - 1
+        product *= numerator / denominator
+    return 2.0 * product
diff --git a/sklearn_questions.py b/sklearn_questions.py
index f65038c6..44bb4a76 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -28,8 +28,12 @@
 from sklearn.utils.multiclass import check_classification_targets
 
 
-class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
+    """OneNearestNeighbor classifier.
+
+    A sample based on the class of its nearest neighbor in the training
+    set, using Euclidean distance.
+    """
 
     def __init__(self):  # noqa: D107
         pass
@@ -37,38 +41,70 @@ def __init__(self):  # noqa: D107
     def fit(self, X, y):
         """Write docstring.
 
-        And describe parameters
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training samples.
+        y : array-like of shape (n_samples,)
+            Target values (class labels).
         """
         X, y = check_X_y(X, y)
         check_classification_targets(y)
         self.classes_ = np.unique(y)
         self.n_features_in_ = X.shape[1]
+        self.X_train_ = X
+        self.y_train_ = y
 
-        # XXX fix
         return self
 
     def predict(self, X):
         """Write docstring.
 
-        And describe parameters
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Samples to predict.
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Predicted class labels.
         """
         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 in range(len(X)):
+            distances = np.sqrt(np.sum((self.X_train_ - X[i]) ** 2, axis=1))
+            nearest_idx = np.argmin(distances)
+            y_pred[i] = self.y_train_[nearest_idx]
+
         return y_pred
 
     def score(self, X, y):
         """Write docstring.
 
-        And describe parameters
+        Parameters
+        ----------
+        X : array of shape (n_samples, n_features)
+            Test samples.
+        y : array of shape (n_samples,)
+            True labels for X.
+
+        Returns
+        -------
+        score : float
+            Mean accuracy of self.predict(X) with respect to 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)