diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..f01c2eb8 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -40,7 +40,11 @@ def max_index(X):
     i = 0
     j = 0
 
-    # TODO
+    if not isinstance(X, np.ndarray) or X.ndim != 2:
+        raise ValueError("Input must be a 2D numpy array.")
+
+    max_array = np.argmax(X)
+    i, j = np.unravel_index(max_array, X.shape)
 
     return i, j
 
@@ -62,6 +66,13 @@ def wallis_product(n_terms):
     pi : float
         The approximation of order `n_terms` of pi using the Wallis product.
     """
+    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 = np.arange(1, n_terms + 1)
+    product = np.prod((4 * k**2) / (4 * k**2 - 1))
+    pi_approximation = 2 * product
     # XXX : The n_terms is an int that corresponds to the number of
     # terms in the product. For example 10000.
-    return 0.
+    return pi_approximation
diff --git a/sklearn_questions.py b/sklearn_questions.py
index 948c2e54..8fc4ee8f 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -20,56 +20,100 @@
 `pydocstyle` that you can also call at the root of the repo.
 """
 import numpy as np
-from sklearn.base import BaseEstimator
-from sklearn.base import ClassifierMixin
-from sklearn.utils.validation import check_X_y
-from sklearn.utils.validation import check_array
-from sklearn.utils.validation import check_is_fitted
-from sklearn.utils.validation import validate_data
+from sklearn.base import BaseEstimator, ClassifierMixin
+from sklearn.metrics.pairwise import pairwise_distances
 from sklearn.utils.multiclass import check_classification_targets
+from sklearn.utils.validation import check_is_fitted, validate_data
 
 
 class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier.
+
+    Parameters
+    ----------
+    None
+
+    Attributes
+    ----------
+    classes_ : ndarray of shape (n_classes,)
+        The unique class labels observed during fitting.
+    n_features_in_ : int
+        Number of features seen during ``fit``.
+    X_ : ndarray of shape (n_samples, n_features)
+        The training input samples.
+    y_ : ndarray of shape (n_samples,)
+        The target values corresponding to training samples.
+    """
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Fit the OneNearestNeighbor classifier from the training dataset.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Training data.
+        y : ndarray of shape (n_samples,)
+            Target values.
+
+        Returns
+        -------
+        self : OneNearestNeighbor
+            The fitted estimator.
         """
-        X, y = check_X_y(X, y)
+        X, y = validate_data(self, X=X, y=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.
+        """Predict the class labels for the provided data.
 
-        And describe parameters
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Test samples.
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Class labels for each data sample.
         """
         check_is_fitted(self)
-        X = check_array(X)
+        X = validate_data(self, X=X, reset=False)
         y_pred = np.full(
             shape=len(X), fill_value=self.classes_[0],
             dtype=self.classes_.dtype
         )
+        distances = pairwise_distances(X, self.X_, metric='euclidean')
+        closest_indices = np.argmin(distances, axis=1)
+        y_pred[:] = self.y_[closest_indices]
 
-        # XXX fix
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Return the mean accuracy on the given test data and labels.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Test samples.
+        y : array-like 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)
+        X, y = validate_data(self, X=X, y=y, reset=False)
         y_pred = self.predict(X)
+        y_score = (y_pred == y).sum() / len(y)
 
-        # XXX fix
-        return y_pred.sum()
+        return y_score