diff --git a/Restaurant/Natalia/Nowy.csv b/Restaurant/Natalia/Nowy.csv
index b3e0c52..f75b589 100644
--- a/Restaurant/Natalia/Nowy.csv
+++ b/Restaurant/Natalia/Nowy.csv
@@ -1,100 +1,121 @@
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diff --git a/Restaurant/Natalia/Tree_natalia b/Restaurant/Natalia/Tree_natalia
deleted file mode 100644
index d5bb46a..0000000
--- a/Restaurant/Natalia/Tree_natalia
+++ /dev/null
@@ -1,54 +0,0 @@
-# Load libraries
-import pandas as pd
-from sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier
-from sklearn.model_selection import train_test_split # Import train_test_split function
-from sklearn import metrics #Import scikit-learn metrics module for accuracy calculation
-from sklearn.tree import export_graphviz
-from sklearn.externals.six import StringIO
-from IPython.display import Image
-import pydotplus
-
-col_names = ['age', 'sex', 'fat', 'spicy', 'hungry', 'budget']
-# load dataset
-pima = pd.read_csv("nazwa.csv", header=None, names=col_names)
-
-#split dataset in features and target variable
-feature_cols = ['age', 'sex', 'fat', 'spicy']
-X = pima[feature_cols] # Features
-y = pima.label # Target variable
-
-# Split dataset into training set and test set
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) # 70% training and 30% test
-
-# Create Decision Tree classifer object
-clf = DecisionTreeClassifier()
-
-# Train Decision Tree Classifer
-clf = clf.fit(X_train,y_train)
-
-#Predict the response for test dataset
-y_pred = clf.predict(X_test)
-
-# Model Accuracy, how often is the classifier correct?
-print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
-
-dot_data = StringIO()
-export_graphviz(clf, out_file=dot_data,
-                filled=True, rounded=True,
-                special_characters=True,feature_names = feature_cols,class_names=['0','1'])
-graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
-graph.write_png('food_tree.png')
-Image(graph.create_png())
-
-
-# Create Decision Tree classifer object
-clf = DecisionTreeClassifier(criterion="entropy", max_depth=3)
-
-# Train Decision Tree Classifer
-clf = clf.fit(X_train,y_train)
-
-#Predict the response for test dataset
-y_pred = clf.predict(X_test)
-
-# Model Accuracy, how often is the classifier correct?
-print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
\ No newline at end of file
diff --git a/Restaurant/Natalia/__init__.py b/Restaurant/Natalia/__init__.py
index e69de29..67ffc7c 100644
--- a/Restaurant/Natalia/__init__.py
+++ b/Restaurant/Natalia/__init__.py
@@ -0,0 +1,87 @@
+import pandas as pd
+try:
+    from StringIO import StringIO
+except ImportError:
+    from io import StringIO
+from io import StringIO
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.model_selection import train_test_split
+from sklearn import metrics
+from sklearn.tree import export_graphviz
+import joblib
+from IPython.display import Image
+import pydotplus
+import os
+os.environ["PATH"] += os.pathsep + r'C:\Program Files (x86)\Graphviz2.38\bin'
+
+def main():
+    dot_data = StringIO()
+    col_names = ['age', 'sex', 'fat', 'fiber', 'spicy', 'number']
+    #import danych
+    model_tree = pd.read_csv("Nowy.csv", header=None, names=col_names)
+    model_tree.head()
+    #seperacja danych cechy
+    feature_cols = ['age', 'sex', 'fat', 'fiber', 'spicy']
+    X = model_tree[feature_cols]
+    #separacja danych etykieta
+    y = model_tree.number
+    # Target variable
+
+    #podział danych na zestaw treningowy i testowy; 70% trening 30% test
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,
+                                                        random_state=1)  # 70% training and 30% test
+
+    ###############################
+    # stworzenie -obiektu- drzewa Decision Tree classifer
+    clf = DecisionTreeClassifier()
+
+    # drzewo treningowe
+    clf = clf.fit(X_train, y_train)
+
+    #generowanie wyników dla zbioru testowego
+    y_pred = clf.predict(X_test)
+    #print(y_pred)
+
+    #Akuratność dla modelu danych
+    print("Accuracy:", metrics.accuracy_score(y_test, y_pred))
+
+    dot_data = StringIO()
+    #tworzenie graficznego drzewa
+    export_graphviz(clf, out_file=dot_data,
+                    filled=True, rounded=True,
+                    special_characters=True, feature_names=feature_cols,
+                    class_names=['1', '2', '3', '4', '5', '6', '7'])
+    graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
+    graph.write_png('polecanie_1.png')
+    Image(graph.create_png())
+
+    # ************************************************************
+    # stworzenie -obiektu- drzewa Decision Tree classifer z kryterium entropii
+    clf = DecisionTreeClassifier(criterion="entropy")
+
+    #drzewo testowe-z entripią
+    clf = clf.fit(X_train, y_train)
+
+    #generowanie wyników dla zbioru testowego
+    y_pred = clf.predict(X_test)
+
+    #Akuratność dla modelu danych z warunkiem entropii
+    print("Accuracy:", metrics.accuracy_score(y_test, y_pred))
+
+    #stowrzenie graficznego drzewa z warunkiem entropii
+    dot_data = StringIO()
+    export_graphviz(clf, out_file=dot_data,
+                    filled=True, rounded=True,
+                    special_characters=True, feature_names=feature_cols,
+                    class_names=['1', '2', '3', '4', '5', '6', '7'])
+    graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
+    graph.write_png('polecanie_2_entropia.png')
+    Image(graph.create_png())
+
+    #zapisanie modelu danych do pliku
+    file_name = 'final_model.sav'
+    joblib.dump(clf, file_name)
+
+    return
+
+main()
\ No newline at end of file
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