Check for NaNs in the data, since sklearn.LinearRegression cannot handle them.

machine_learning/pipeline.py

@@ -180,12 +180,15 @@ class ModelValidation:
 
     def cross_validate(self):
         if self.model is None:
-            raise ValueError(
+            raise TypeError(
                 "Please set self.model first, e.g. self.model = sklearn.linear_model.LinearRegression()"
             )
-        # TODO Is ValueError appropriate here?
         if self.cv is None:
-            raise ValueError("Please use set_cv_method() first.")
+            raise TypeError("Please use set_cv_method() first.")
+        if self.X.isna().any().any() or self.y.isna().any().any():
+            raise ValueError(
+                "NaNs were found in either X or y. Please, check your data before continuing."
+            )
         return cross_val_score(
             estimator=self.model,
             X=self.X,