diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..45d98801 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.
     """
+    # TODO
+
+    if not isinstance(X, np.ndarray):
+        raise ValueError("The input should be a numpy array.")
+    if X.ndim != 2:
+        raise ValueError("The input should be a 2D numpy array.")
     i = 0
     j = 0
-
-    # TODO
+    for row in range(X.shape[0]):
+        for col in range(X.shape[1]):
+            if X[row, col] > X[i, j]:
+                i = row
+                j = col
 
     return i, j
 
@@ -64,4 +73,12 @@ 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.
+    if n_terms < 0:
+        raise ValueError("The number of terms should be a positive integer.")
+    if n_terms == 0:
+        return 1
+    pi_approx = 1
+    for k in range(1, n_terms + 1):
+        product = (4 * k ** 2) / (4 * k ** 2 - 1)
+        pi_approx = pi_approx * product
+    return 2*pi_approx
diff --git a/sklearn_questions.py b/sklearn_questions.py
index 948c2e54..c37001f7 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -23,53 +23,78 @@
 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.utils.multiclass import check_classification_targets
 
 
 class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
-    "OneNearestNeighbor classifier."
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
+        """Fit the model using X as training data and y as target values.
 
-        And describe parameters
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features) (Training data.)
+        y : ndarray of shape (n_samples,) (Target values.)
+
+        Returns
+        -------
+        self : object (Returns the instance itself.)
         """
-        X, y = check_X_y(X, y)
+        X, y = validate_data(self, 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.
+        """Predict the class labels for the provided data.
 
-        And describe parameters
+        Parameters
+        ----------
+        X : ndarray of shape (n_queries, n_features)
+            Test samples.
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_queries,)
+            Class labels for each data sample.
         """
         check_is_fitted(self)
-        X = check_array(X)
+        X = validate_data(self, X, reset=False)
         y_pred = np.full(
             shape=len(X), fill_value=self.classes_[0],
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        for i, x_i in enumerate(X):
+            distances = np.linalg.norm(self.X_ - x_i, axis=1)
+            nearest_idx = np.argmin(distances)
+            y_pred[i] = self.y_[nearest_idx]
         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 : ndarray of shape (n_samples, n_features)
+            Test samples.
+        y : ndarray 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)