commit_natalia

.idea/DSZI.iml

@@ -5,7 +5,7 @@
       <excludeFolder url="file://$MODULE_DIR$/Restaurant/Marta/venv" />
       <excludeFolder url="file://$MODULE_DIR$/venv" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.7" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.7 (projekt_sztuczna)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
   <component name="TestRunnerService">

.idea/misc.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7 (projekt_sztuczna)" project-jdk-type="Python SDK" />
 </project>

Restaurant/Natalia/Nowy.csv

@@ -0,0 +1,100 @@
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Restaurant/Natalia/Tree_natalia

@@ -0,0 +1,54 @@
+# 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))