diff --git a/numpy_questions.py b/numpy_questions.py
index 21fcec4b..894176ce 100644
--- a/numpy_questions.py
+++ b/numpy_questions.py
@@ -15,6 +15,7 @@
 This will be enforced with `flake8`. You can check that there is no flake8
 errors by calling `flake8` at the root of the repo.
 """
+
 import numpy as np
 
 
@@ -40,8 +41,17 @@ def max_index(X):
     i = 0
     j = 0
 
-    # TODO
+    # check that X is a numpy array
+    if not isinstance(X, np.ndarray):
+        raise ValueError("X must be a numpy array.")
+
+    # check it has 2 dimensions
+    if len(X.shape) != 2:
+        raise ValueError("X must be 2-dimensional.")
 
+    max_value = np.argmax(X)  # find the maximum
+
+    i, j = np.unravel_index(max_value, X.shape)  # find where is the maximum
     return i, j
 
 
@@ -62,6 +72,11 @@ 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
+    n = np.arange(1, n_terms + 1)  # the range for n (times we multiply)
+    wallis_product = np.prod((4 * n**2) / (4 * n**2 - 1))
+    return wallis_product * 2  # we multiply by two to find pi
diff --git a/sklearn_questions.py b/sklearn_questions.py
index f65038c6..fac12ccd 100644
--- a/sklearn_questions.py
+++ b/sklearn_questions.py
@@ -19,56 +19,86 @@
 for the methods you code and for the class. The docstring will be checked using
 `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.multiclass import check_classification_targets
 
 
-class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
+    """One Nearest Neighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
+        """Fit the OneNearestNeighbor class.
+
+        Parameters
+        ----------
+            X : array, shape(n_samples, n_features) - training data
+            Y : array, shape(n_samples, n_features) - target
 
-        And describe parameters
+        Return
+        ----------
+            self : OneNearestNeighbor - the 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_train_ = X
+        self.y_train_ = y
+
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Prediction of the OneNearestNeighbor class.
+
+        Parameters
+        ----------
+            X : array, shape(n_samples, n_features) - test data
+
 
-        And describe parameters
+        Return
+        ----------
+            y_pred : array, shape(n_samples,) -
+              the predicted class for each sample
         """
         check_is_fitted(self)
-        X = check_array(X)
+        X = self._validate_data(X, reset=False)
+
         y_pred = np.full(
             shape=len(X), fill_value=self.classes_[0],
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        for i, x in enumerate(X):
+            distances = np.sqrt(np.sum((self.X_train_ - x) ** 2, axis=1))
+            nearest_index = np.argmin(distances)
+            y_pred[i] = self.y_train_[nearest_index]
+
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
+        """Give accuracy score.
+
+        Parameters
+        ----------
+            X : array, shape(n_samples, n_features) - test data
+            y : array, shape(n_samples, n_features) - train data
+
+        Return
+        ----------
+            accuracy : float - the mean accuracy for the prediction
 
-        And describe parameters
         """
         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