From 3a8cdc3e932601976c88ea805415a1423d747edf Mon Sep 17 00:00:00 2001
From: Mirrowel <Mirrowel.zz@gmail.com>
Date: Thu, 18 May 2023 20:49:16 +0200
Subject: [PATCH] Tree #2

---
 1.py           | 25 -------------------------
 TreeConcept.py | 30 ++++++++++++++++++++++++++++++
 2 files changed, 30 insertions(+), 25 deletions(-)
 delete mode 100644 1.py
 create mode 100644 TreeConcept.py

diff --git a/1.py b/1.py
deleted file mode 100644
index eecf7d7..0000000
--- a/1.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from sklearn.datasets import load_iris
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.model_selection import train_test_split
-from sklearn import metrics
-
-# Load the Iris dataset (or you can use your own dataset)
-iris = load_iris()
-X = iris.data
-y = iris.target
-
-# Split the dataset into training and testing sets
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-# Create an instance of the DecisionTreeClassifier
-clf = DecisionTreeClassifier()
-
-# Train the decision tree classifier
-clf.fit(X_train, y_train)
-
-# Make predictions on the testing set
-y_pred = clf.predict(X_test)
-
-# Evaluate the accuracy of the model
-accuracy = metrics.accuracy_score(y_test, y_pred)
-print("Accuracy:", accuracy)
\ No newline at end of file
diff --git a/TreeConcept.py b/TreeConcept.py
new file mode 100644
index 0000000..2335209
--- /dev/null
+++ b/TreeConcept.py
@@ -0,0 +1,30 @@
+from sklearn import tree
+
+# Define the training dataset with 8 attributes and corresponding labels
+training_data = [
+    [1, 0, 0, 1, 0, 1, 1, 'A'],
+    [1, 0, 0, 0, 1, 1, 1, 'A'],
+    [0, 1, 0, 1, 0, 1, 1, 'B'],
+    [0, 0, 0, 1, 0, 0, 1, 'B'],
+    [0, 1, 1, 0, 1, 0, 0, 'B'],
+    [1, 0, 0, 0, 1, 0, 1, 'A'],
+    [0, 0, 0, 1, 0, 0, 0, 'B'],
+    [1, 1, 0, 1, 1, 1, 0, 'A'],
+    [0, 0, 0, 0, 0, 0, 1, 'B'],
+    [0, 0, 1, 0, 0, 1, 0, 'B']
+]
+
+# Separate the attributes and labels
+X_train = [data[:-1] for data in training_data]
+y_train = [data[-1] for data in training_data]
+
+# 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)
+
+# Test the decision tree with a new example
+new_example = [1, 0, 0, 1, 1, 0, 0]  # Example with 8 attributes
+predicted_label = clf.predict([new_example])
+print("Predicted Label:", predicted_label[0])
\ No newline at end of file