diff --git a/decisiontree.py b/decisiontree.py
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index 0000000..ea63ace
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+++ b/decisiontree.py
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+import pandas as pd 
+import matplotlib.pyplot as plt 
+from sklearn.model_selection import train_test_split
+import category_encoders as ce
+from sklearn.metrics import accuracy_score
+from sklearn.tree import DecisionTreeClassifier
+from sklearn import tree
+import random
+import graphviz
+import warnings
+warnings.filterwarnings('ignore')
+
+data = './database/datasetadult.csv'
+df = pd.read_csv(data)
+#print(df.shape)
+#print(df.head())
+#print(df.info())
+#print(df['Outfit'].value_counts())
+#print(df.isnull().sum())
+
+X = df.drop(['Adult'], axis=1)
+
+y = df['Adult']
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 42)
+#print(X_train.shape, X_test.shape)
+#print(X_train.dtypes)
+#print(X_train.head())
+
+encoder = ce.OrdinalEncoder(cols=['Wrinkles', 'Balding', 'Beard', 'Outfit', 'Glasses', 'Tattoo', 'Hair', 'Behaviour'])
+
+X_train = encoder.fit_transform(X_train)
+
+X_test = encoder.transform(X_test)
+
+#print(X_train.head())
+#print(X_test.head())
+clf_en = DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)
+clf_en.fit(X_train, y_train)
+
+y_pred_en = clf_en.predict(X_test)
+print('Model accuracy score with criterion entropy: {0:0.4f}'.format(accuracy_score(y_test, y_pred_en)))
+
+y_pred_train_en = clf_en.predict(X_train)
+print('Training-set accuracy score: {0:0.4f}'.format(accuracy_score(y_train, y_pred_train_en)))
+
+print('Training set score: {:.4f}'.format(clf_en.score(X_train, y_train)))
+print('Test set score: {:.4f}'.format(clf_en.score(X_test, y_test)))
+
+dot_data = tree.export_graphviz(clf_en, out_file=None, 
+                              feature_names=X_train.columns,  
+                              class_names=y_train.unique(),  
+                              filled=True, rounded=True,  
+                              special_characters=True)
+
+#nowy klient testowo
+new_client = {
+    "Wrinkles": random.choice(['Yes', 'No']),
+    "Balding": random.choice(['Yes', 'No']),
+    "Beard": random.choice(['Yes', 'No']),
+    "Outfit": random.choice(['Messy', 'Casual','Formal']),
+    "Glasses": random.choice(['Yes', 'No']),
+    "Tattoo": random.choice(['Yes', 'No']),
+    "Hair": random.choice(['Color', 'Grey', 'Natural']),
+    "Behaviour": random.choice(['Energetic', 'Stressed', 'Calm'])
+}
+
+new_client_df = pd.DataFrame(new_client, index=[0])
+new_client_df_encoded = encoder.transform(new_client_df)
+prediction = clf_en.predict(new_client_df_encoded)
+
+print("\nNew client:")
+print(new_client_df)
+print("Prediction:", prediction[0])
+
+graph = graphviz.Source(dot_data) 
+graph.render("decision_tree", format='png')