diff --git a/.gitignore b/.gitignore
index 686ca0d..56bad65 100644
--- a/.gitignore
+++ b/.gitignore
@@ -56,4 +56,9 @@ docs/_build/
 # Pipfiles
 
 Pipfile
-Pipfile.lock
\ No newline at end of file
+Pipfile.lock
+decision_tree
+decision_tree.pdf
+Source.gv.pdf
+Source.gv
+decision_tree.txt
diff --git a/TreeConcept.py b/TreeConcept.py
new file mode 100644
index 0000000..6f6e857
--- /dev/null
+++ b/TreeConcept.py
@@ -0,0 +1,38 @@
+from sklearn import tree
+import pandas as pd #for manipulating the csv data
+import numpy as np
+import graphviz
+import os
+os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz/bin/'
+
+#importing the dataset from the disk
+train_data_m=np.genfromtxt("dataset/converted_dataset.csv", delimiter=",",skip_header=1);
+
+# Separate the attributes and labels
+X_train = [data[:-1] for data in train_data_m]
+y_train = [data[-1] for data in train_data_m]
+
+# Create the decision tree classifier using the ID3 algorithm
+clf = tree.DecisionTreeClassifier(criterion='entropy')
+#clf = tree.DecisionTreeClassifier(criterion='gini')
+
+# Train the decision tree on the training data
+clf.fit(X_train, y_train)
+
+# Visualize the trained decision tree
+tree_text = tree.export_text(clf,feature_names=['Battery Charge', 'Fullness', 'Ready orders', 'Waiting tables','Availability', 'Cleanliness', 'Error'])
+with open('decision_tree.txt', 'w') as f:
+    f.write(tree_text) # Save the visualization as a text file
+dot_data = tree.export_graphviz(clf, out_file=None, feature_names=['Battery Charge', 'Fullness', 'Ready orders', 'Waiting tables','Availability', 'Cleanliness', 'Error'], class_names=['NO', 'YES'], filled=True,rounded=True)
+graph = graphviz.Source(dot_data)
+graph.render("decision_tree")  # Save the visualization as a PDF file
+
+# Test the decision tree with a new example
+#Battery Charge,Fullness,Ready orders,Waiting tables,Availability,Cleanliness,Error
+new_example = [2, 0, 1, 1, 1 ,2, 0]
+predicted_label = clf.predict([new_example])
+if predicted_label[0]>0:
+    result="YES"
+else:
+    result="NO"
+print("Predicted Label:", result)
\ No newline at end of file
diff --git a/dataset/Dataset.csv b/dataset/Dataset.csv
new file mode 100644
index 0000000..ab034bb
--- /dev/null
+++ b/dataset/Dataset.csv
@@ -0,0 +1,201 @@
+Battery Charge,Fullness,Ready orders,Waiting tables,Availability,Cleanliness,Error,To go
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diff --git a/dataset/converted_dataset.csv b/dataset/converted_dataset.csv
new file mode 100644
index 0000000..ff324f2
--- /dev/null
+++ b/dataset/converted_dataset.csv
@@ -0,0 +1,200 @@
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+0,1,1,0,0,2,1,0
+1,0,1,0,0,0,1,0
+0,1,1,1,1,0,1,0
+2,0,1,1,0,1,0,0
+2,0,0,0,0,2,0,0
+2,1,1,1,0,1,0,0
+0,1,1,1,1,1,0,0
+1,0,1,0,1,0,0,0
diff --git a/dataset/datasetConverter.py b/dataset/datasetConverter.py
new file mode 100644
index 0000000..ade5bca
--- /dev/null
+++ b/dataset/datasetConverter.py
@@ -0,0 +1,54 @@
+import csv
+
+def convert_dataset(input_file, output_file):
+    attributes = {
+        1: ["empty", "low", "medium", "high"],
+        2: ["empty", "full"],
+        3: ["none", "available"],
+        4: ["none", "available"],
+        5: ["unavailable", "available"],
+        6: ["low", "medium", "high"],
+        7: ["no", "yes"],
+        8: ["no", "yes"]
+    }
+    
+    # Create a mapping dictionary for attribute values
+    mapping = {}
+    for attr, values in attributes.items():
+        mapping[attr] = {value: index for index, value in enumerate(values)}
+    
+    converted_dataset = []
+    
+    # Read the input CSV file
+    with open(input_file, "r") as csvfile:
+        reader = csv.reader(csvfile)
+        
+        header = next(reader)  # Skip the header row
+        
+        # Convert the data rows
+        for row in reader:
+            converted_row = []
+            for i, value in enumerate(row):
+                # Convert the attribute values
+                if i + 1 in mapping:
+                    converted_value = mapping[i + 1][value]
+                else:
+                    converted_value = value
+                
+                converted_row.append(converted_value)
+            
+            converted_dataset.append(converted_row)
+    
+    # Write the converted dataset to a new CSV file
+    with open(output_file, "w", newline="") as csvfile:
+        writer = csv.writer(csvfile)
+        
+        # Write the header row
+        #writer.writerow(header)
+        
+        # Write the converted data rows
+        for row in converted_dataset:
+            writer.writerow(row)
+
+# Example usage:
+convert_dataset("dataset/Dataset.csv", "dataset/converted_dataset.csv")
\ No newline at end of file
diff --git a/dataset/datasetGenerator.py b/dataset/datasetGenerator.py
new file mode 100644
index 0000000..b525b04
--- /dev/null
+++ b/dataset/datasetGenerator.py
@@ -0,0 +1,57 @@
+import random
+import csv
+
+attributes = {
+    1: ["empty", "low", "medium", "high"],
+    2: ["empty", "full"],
+    3: ["none", "available"],
+    4: ["none", "available"],
+    5: ["unavailable", "available"],
+    6: ["low", "medium", "high"],
+    7: ["no", "yes"],
+    8: ["no", "yes"]
+}
+
+dataset = []
+while len(dataset) < 200:
+    data = {}
+    
+    # Generate random values for attributes 1-7
+    for attr in range(1, 8):
+        data[attr] = random.choice(attributes[attr])
+    
+    # Apply the rules to determine the value of attribute 8
+    if data[1] in ["empty", "low"]:
+        data[8] = "no"
+    elif data[2] == "full" and data[4] == "none":
+        data[8] = "no"
+    elif data[2] == "empty" and data[3] == "none":
+        data[8] = "no"
+    elif data[3] == "none" and data[4] == "none":
+        data[8] = "no"
+    elif data[5] == "unavailable":
+        data[8] = "no"
+    elif data[6] == "low":
+        data[8] = "no"
+    elif data[7] == "yes":
+        data[8] = "no"
+    else:
+        data[8] = "yes"
+    
+    # Check if the generated data already exists in the dataset
+    if data not in dataset:
+        dataset.append(data)
+
+# Print the generated dataset
+for data in dataset:
+    print(data)
+with open("dataset/Dataset.csv", "w", newline="") as csvfile:
+    writer = csv.writer(csvfile)
+    
+    # Write the header row
+    writer.writerow(["Battery Charge", "Fullness", "Ready orders", "Waiting tables","Availability", "Cleanliness", "Error", "To go"])
+    
+    # Write the data rows
+    for data in dataset:
+        row = [data[attr] for attr in range(1, 9)]
+        writer.writerow(row)
\ No newline at end of file
diff --git a/src/decisionTree/dataset_generator.py b/src/decisionTree/dataset_generator.py
new file mode 100644
index 0000000..99b7a1f
--- /dev/null
+++ b/src/decisionTree/dataset_generator.py
@@ -0,0 +1,33 @@
+import csv
+import random
+
+battery_values = ['high', 'medium', 'low']
+distance_values = ['far', 'medium', 'close']
+mood_values = ['good', 'medium', 'bad']
+other = ['yes', 'no']
+
+
+training_data = []
+
+for _ in range(200):
+    battery = random.choice(battery_values)
+    distance = random.choice(distance_values)
+    mood = random.choice(mood_values)
+    memory = random.randint(0,4)
+    dishes_held = random.randint(0,4)
+    """empty_basket = random.choice(other)
+    if empty_basket == 'yes':
+        dish_in_basket = 'no'
+    else:
+        dish_in_basket = random.choice(other)
+    dish_in_basket = random.choice(other)"""
+    waiting_for_order = random.choice(other)
+    waiting_for_dish = random.choice(other)
+
+    example = [battery, distance, mood, memory, dishes_held, waiting_for_order, waiting_for_dish]
+    training_data.append(example)
+
+with open('data.csv', 'w', newline='') as file:
+    writer = csv.writer(file)
+    writer.writerows(training_data)
+
diff --git a/src/obj/Waiter.py b/src/obj/Waiter.py
index 04a9be4..2090958 100644
--- a/src/obj/Waiter.py
+++ b/src/obj/Waiter.py
@@ -22,6 +22,89 @@ class Waiter(Object):
 
     def dish_in_basket(self, table) -> bool:
         return table in self.basket
+        self.calcTree()
+        return state
+
+    def calcTree(self):
+        from sklearn import tree
+        import pandas as pd  # for manipulating the csv data
+        import numpy as np
+        import os
+
+        # importing the dataset from the disk
+        train_data_m = np.genfromtxt(
+            "dataset/converted_dataset.csv", delimiter=",", skip_header=1)
+
+        X_train = [data[:-1] for data in train_data_m]
+        y_train = [data[-1] for data in train_data_m]
+        # Create the decision tree classifier using the ID3 algorithm
+        clf = tree.DecisionTreeClassifier(criterion='entropy')
+
+        # Train the decision tree on the training data
+        clf.fit(X_train, y_train)
+
+        # Visualize the trained decision tree
+        tree_text = tree.export_text(clf, feature_names=[
+                                     'Battery Charge', 'Fullness', 'Ready orders', 'Waiting tables', 'Availability', 'Cleanliness', 'Error'])
+        with open('decision_tree.txt', 'w') as f:
+            f.write(tree_text)  # Save the visualization as a text file
+
+        # Test the decision tree with a new example
+        # Battery Charge,Fullness,Ready orders,Waiting tables,Availability,Cleanliness,Error
+        new_example = [self.battery, 0, self.orderReadiness,
+                       self.waitingTables, self.availability, self.cleanliness, self.error]
+        predicted_label = clf.predict([new_example])
+        if predicted_label[0] > 0:
+            result = "YES"
+        else:
+            result = "NO"
+        print("Predicted Label:", result)
+
+    def calcTreePDF(self):
+        from sklearn import tree
+        import pandas as pd  # for manipulating the csv data
+        import numpy as np
+        import graphviz
+        import os
+        os.environ["PATH"] += os.pathsep + \
+            'C:/Program Files (x86)/Graphviz/bin/'
+
+        # importing the dataset from the disk
+        train_data_m = np.genfromtxt(
+            "dataset/converted_dataset.csv", delimiter=",", skip_header=1)
+
+        X_train = [data[:-1] for data in train_data_m]
+        y_train = [data[-1] for data in train_data_m]
+        # Create the decision tree classifier using the ID3 algorithm
+        clf = tree.DecisionTreeClassifier(criterion='entropy')
+
+        # Train the decision tree on the training data
+        clf.fit(X_train, y_train)
+
+        # Visualize the trained decision tree
+        tree_text = tree.export_text(clf, feature_names=[
+                                     'Battery Charge', 'Fullness', 'Ready orders', 'Waiting tables', 'Availability', 'Cleanliness', 'Error'])
+        with open('decision_tree.txt', 'w') as f:
+            f.write(tree_text)  # Save the visualization as a text file
+        dot_data = tree.export_graphviz(clf, out_file=None, feature_names=[
+                                        'Battery Charge', 'Fullness', 'Ready orders', 'Waiting tables', 'Availability', 'Cleanliness', 'Error'], class_names=['NO', 'YES'], filled=True, rounded=True)
+        graph = graphviz.Source(dot_data)
+        graph.render("decision_tree")  # Save the visualization as a PDF file
+
+        # Test the decision tree with a new example
+        # Battery Charge,Fullness,Ready orders,Waiting tables,Availability,Cleanliness,Error
+        new_example = [self.battery, 0, self.orderReadiness,
+                       self.waitingTables, self.availability, self.cleanliness, self.error]
+        predicted_label = clf.predict([new_example])
+        if predicted_label[0] > 0:
+            result = "YES"
+        else:
+            result = "NO"
+        print("Predicted Label:", result)
+
+    def dampState(self):
+        self.prev_position = copy.deepcopy(self.position)
+        self.prev_orientation = copy.copy(self.orientation)
 
     def basket_is_full(self) -> bool:
         return self.basket_size == 0