diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..aa1a10df 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -37,12 +37,11 @@ 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) or X.ndim != 2:
+        raise ValueError("X must be a 2D numpy array.")
 
-    # TODO
-
-    return i, j
+    flat_idx = np.argmax(X)
+    return np.unravel_index(flat_idx, X.shape)
 
 
 def wallis_product(n_terms):
@@ -62,6 +61,12 @@ 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 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, dtype=np.float64)
+    terms = (2 * k / (2 * k - 1)) * (2 * k / (2 * k + 1))
+    return 2.0 * np.prod(terms)
diff --git a/sklearn_questions.py b/sklearn_questions.py
index f65038c6..b2fcc6c0 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -22,53 +22,88 @@
 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.multiclass import check_classification_targets
+from sklearn.utils.validation import check_is_fitted
+from sklearn.utils.validation import check_X_y
+from sklearn.utils.validation import check_array
 
 
-class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
+        """Fit the classifier by memorizing the training set.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Training input samples.
+
+        y : ndarray of shape (n_samples,)
+            Target class labels.
 
-        And describe parameters
+        Returns
+        -------
+        self : OneNearestNeighbor
+            Fitted estimator.
         """
         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.
+        """Predict class labels for samples in `X`.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_queries, n_features)
+            Input samples.
 
-        And describe parameters
+        Returns
+        -------
+        y_pred : ndarray of shape (n_queries,)
+            Predicted class labels.
         """
-        check_is_fitted(self)
+        check_is_fitted(
+            self, attributes=["X_", "y_", "classes_", "n_features_in_"]
+            )
         X = check_array(X)
-        y_pred = np.full(
-            shape=len(X), fill_value=self.classes_[0],
-            dtype=self.classes_.dtype
-        )
+        if X.shape[1] != self.n_features_in_:
+            raise ValueError(
+                f"X has {X.shape[1]} features, but {self.__class__.__name__} "
+                f"is expecting {self.n_features_in_} features as input"
+            )
 
-        # XXX fix
-        return y_pred
+        diff = X[:, np.newaxis, :] - self.X_[np.newaxis, :, :]
+        dists = np.sum(diff ** 2, axis=2)
+        nn_idx = np.argmin(dists, axis=1)
+        return self.y_[nn_idx]
 
     def score(self, X, y):
-        """Write docstring.
+        """
+        Compute accuracy of the classifier on the given test data and labels.
 
-        And describe parameters
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Test samples.
+
+        y : ndarray of shape (n_samples,)
+            True labels for `X`.
+
+        Returns
+        -------
+        accuracy : float
+            Mean accuracy of predictions on `X` compared to `y`.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
-
-        # XXX fix
-        return y_pred.sum()
+        return float(np.mean(y_pred == y))
diff --git a/students.txt b/students.txt
index 6813300e..d358425e 100644
--- a/students.txt
+++ b/students.txt
@@ -15,7 +15,7 @@ Burtin	Léo
 Chaabouni	Kenza     
 Chauhan	Bhavesh     
 Chou	Wei-Chieh X    
-Clot	Augustin     
+Clot	Augustin   X
 De Sauvan D'Aramon	Ithier     X
 Descazeaud	Lucien X   
 Despréaux	Maxime X    
@@ -60,7 +60,7 @@ Liard	Eléanor X
 Liu	Guangyue    X 
 Liu	Yunxian     X
 Lucille	Maximilien X     
-Mahé	Blanche     
+Mahé	Blanche     X
 Martin	Justin     X
 Massias	Mathurin     
 Massoud	Alexandre.....X