diff --git a/src/SupervisedLearning/LogisticRegression/Validator.py b/src/SupervisedLearning/LogisticRegression/Validator.py
index a50b9bd..0b3a373 100644
--- a/src/SupervisedLearning/LogisticRegression/Validator.py
+++ b/src/SupervisedLearning/LogisticRegression/Validator.py
@@ -1,21 +1,32 @@
 import numpy as np
+from pandas.api.types import is_numeric_dtype
 
 
 class TargetValueError(Exception):
-    def __init__(self, seen, expected):
-        message = "".join(map(str,["Invalid nb of target classes, seen: ",seen ," expected ", expected]))
+    def __init__(self, received, expected):
+        message = "".join(map(str,["Invalid number of target classes, received: ", received ," expected: ", expected]))
         super(TargetValueError, self).__init__(message)
 
 
 class DataDimError(Exception):
-    def __init__(self, seen, expected):
-        message = "".join(map(str, ["Invalid number of features, seen: ", seen," expected ", expected]))
+    def __init__(self, expected, received):
+        message = "".join(map(str, ["Invalid number of features, expected: ", expected, " received: ", received]))
         super(DataDimError, self).__init__(message)
 
 class DataTargetMissmatch(Exception):
     def __init__(self, data, target):
-        message = "".join(map(str, ["Number of data examples ", data ," does not match target ", target," examples"]))
+        message = "".join(map(str, ["Number of data examples: ", data ," does not match target: ", target," examples"]))
         super(DataTargetMissmatch, self).__init__(message)
+        
+class TargetDataCatError(Exception):
+    def __init__(self, target):
+        message = "".join(map(str,["Invalid type of target classes, received: ", target ," expected cat {0, 1} "]))
+        super(TargetDataCatError, self).__init__(message)
+        
+class DataTypeError(Exception):
+    def __init__(self):
+        message = "".join(map(str,["Invalid type of data, expected numerical."]))
+        super(DataTypeError, self).__init__(message) 
 
 
 class LogRegValidator:
@@ -25,16 +36,26 @@ def __init__(self, n_features, n_classes):
 
     def validate_training(self, data, target):
         self.__validate_target(target)
+        self.__validate_target_type(target)
         self.__validate_data(data)
+        self.__validate_data_type(data)
         self.__check_if_data_and_target_match(data, target)
 
     def __validate_target(self, target):
         if np.unique(target).shape[0] > self.n_classes:
             raise TargetValueError(np.unique(target).shape[0], self.n_classes)
 
+    def __validate_target_type(self, target):
+        if set(target) != {0, 1}:
+            raise TargetDataCatError(set(target))
+            
     def __validate_data(self, data):
         if data.shape[1] != self.n_features:
-            raise DataDimError(data.shape[0], self.n_features)
+            raise DataDimError(data.shape[1], self.n_features)
+            
+    def __validate_data_type(self, data):
+        if set([is_numeric_dtype(data[e]) for e in data.columns]) != {True}:
+            raise DataTypeError()
 
     def __check_if_data_and_target_match(self, data, target):
         if data.shape[0] != target.shape[0]: