diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..9e4a3cd7 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -37,10 +37,19 @@ def max_index(X):
         If the input is not a numpy array or
         if the shape is not 2D.
     """
+    if not isinstance(X, np.ndarray):
+        raise ValueError("Input must be a NumPy array.")
+
+    if len(X.shape) != 2:
+        raise ValueError("Input should be a 2D numpy array.")
+
     i = 0
     j = 0
 
     # TODO
+    i, j = np.unravel_index(np.argmax(X, axis=None), X.shape)
+    # i = int(ind[0])
+    # j = int(ind[1])
 
     return i, j
 
@@ -64,4 +73,15 @@ 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:
+        return 1.0
+
+    product = 1.0
+    for i in range(1, n_terms + 1):
+        numerator = 4 * i**2
+        denominator = 4 * i**2 - 1
+        product *= numerator / denominator
+
+    pi = 2 * product
+    return pi
diff --git a/sklearn_questions.py b/sklearn_questions.py
index f65038c6..5f995168 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -29,28 +29,48 @@
 
 
 class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Fit the model given the training samples X and training targets y.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Array of the training samples.
+        y : ndarray of shape (n_samples,)
+            Array of the training targets.
+
+        Returns
+        -------
+        self : object
+            The fitted classifier.
         """
         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.
+        """Find index of closest training sample and assign label to target.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Array of samples to predict.
 
-        And describe parameters
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Array of the predicted targets.
         """
         check_is_fitted(self)
         X = check_array(X)
@@ -59,16 +79,30 @@ def predict(self, X):
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        for i in range(X.shape[0]):
+            distances = np.linalg.norm(self.X_ - X[i, :], axis=1)
+            nearest_index = np.argmin(distances)
+            y_pred[i] = self.y_[nearest_index]
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Compute the number of correct predictions.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            The test samples.
+        y : ndarray of shape (n_samples,)
+            The true labels.
+
+        Returns
+        -------
+        float
+            Accuracy of classifier.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        accuracy = np.mean(y_pred == y)
+
+        return accuracy