diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..a933c24a 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -37,10 +37,16 @@ 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 must be a numpy array.")
 
-    # TODO
+    if X.ndim != 2:
+        raise ValueError("Input array's shape must be 2D.")
+
+    # find which row contains the maximum, then get its column
+    row = np.argmax(np.max(X, axis=1))
+    col = np.argmax(X[row])
+    i, j = row, col
 
     return i, j
 
@@ -55,7 +61,7 @@ def wallis_product(n_terms):
     ----------
     n_terms : int
         Number of steps in the Wallis product. Note that `n_terms=0` will
-        consider the product to be `1`.
+        consider the procduct to be `1`.
 
     Returns
     -------
@@ -64,4 +70,14 @@ 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.
+    # n_terms cannot be negative (follows by definition)
+    if not isinstance(n_terms, int) or n_terms < 0:
+        raise ValueError("n_terms must be a non-negative integer.")
+    if n_terms == 0:
+        return 1.0
+    # k runs from 1 to n_terms
+    k = np.arange(1, n_terms + 1, dtype=float)
+    terms = (4 * k**2) / (4 * k**2 - 1)
+    product = np.prod(terms)
+
+    return 2 * product
diff --git a/sklearn_questions.py b/sklearn_questions.py
index f65038c6..c88eaa29 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -29,46 +29,102 @@
 
 
 class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+    """One-nearest-neighbor classifier using Euclidean distance."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Fit the one-nearest-neighbor classifier.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Training input.
+        y : ndarray of shape (n_samples,)
+            Target labels.
+
+        Returns
+        -------
+        self : OneNearestNeighbor
+            Fitted estimator.
+
+        Raises
+        ------
+        ValueError
+            If X and y do not have compatible shapes or if y is not suitable
+            for classification.
         """
         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.
-
-        And describe parameters
+        """
+        Predict class labels for samples in X.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Input samples
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Predicted labels
+
+        Raises
+        ------
+        ValueError
+            If the estimator not fitted or X has incorrect shape.
         """
         check_is_fitted(self)
         X = check_array(X)
+
         y_pred = np.full(
-            shape=len(X), fill_value=self.classes_[0],
+            shape=len(X),
+            fill_value=self.classes_[0],
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        # nearest neighbor for each sample using Euclidean distances
+        dist = X[:, np.newaxis, :] - self.X_[np.newaxis, :, :]
+        dists = np.sum(dist ** 2, axis=2)
+
+        # closest training point
+        nn_idx = np.argmin(dists, axis=1)
+
+        # pred
+        y_pred[:] = self.y_[nn_idx]
+
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Compute the mean accuracy on the given test data and labels.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Test input
+        y : ndarray of shape (n_samples,)
+            True labels test
+
+        Returns
+        -------
+        score : float
+            Mean accuracy of the classifier on the test dataset
+
+        Raises
+        ------
+        ValueError
+            If X and y have incompatible shapes.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        # return accuracy
+        return float(np.mean(y_pred == y))