EntropyWorks
diff --git a/‎data-science/books.py renamed to ‎machine-learning/books.py b/‎data-science/books.py renamed to ‎machine-learning/books.py
diff --git a/‎data-science/egrep.py renamed to ‎machine-learning/egrep.py b/‎data-science/egrep.py renamed to ‎machine-learning/egrep.py
diff --git a/‎data-science/exp.py renamed to ‎machine-learning/exp.py b/‎data-science/exp.py renamed to ‎machine-learning/exp.py
diff --git a/‎machine-learning/fruit-classify.py
Lines changed: 51 additions & 0 deletions b/‎machine-learning/fruit-classify.py
Lines changed: 51 additions & 0 deletions
diff --git a/‎data-science/line-count.py renamed to ‎machine-learning/line-count.py b/‎data-science/line-count.py renamed to ‎machine-learning/line-count.py
diff --git a/‎data-science/most_common_words.py renamed to ‎machine-learning/most_common_words.py b/‎data-science/most_common_words.py renamed to ‎machine-learning/most_common_words.py
diff --git a/‎data-science/statistics.py renamed to ‎machine-learning/statistics.py b/‎data-science/statistics.py renamed to ‎machine-learning/statistics.py
@@ -0,0 +1,51 @@
+from sklearn import tree
+
+TEXTURE_BUMPY = 0
+TEXTURE_SMOOTH = 1
+
+LABEL_APPLE = 0
+LABEL_ORANGE = 1
+
+LABELS = {
+    LABEL_APPLE: 'apple',
+    LABEL_ORANGE: 'orange',
+}
+
+
+def classify(to_classify):
+    features = [
+        [90, TEXTURE_BUMPY],
+        [140, TEXTURE_SMOOTH],
+        [130, TEXTURE_SMOOTH],
+        [150, TEXTURE_BUMPY],
+        [170, TEXTURE_BUMPY],
+    ]
+    labels = [
+        LABEL_ORANGE,
+        LABEL_APPLE,
+        LABEL_APPLE,
+        LABEL_ORANGE,
+        LABEL_ORANGE
+    ]
+
+    clf = tree.DecisionTreeClassifier()
+    clf = clf.fit(features, labels)
+
+    return clf.predict(to_classify)
+
+
+def main():
+    to_classify = [
+        [145, TEXTURE_BUMPY],
+        [120, TEXTURE_SMOOTH],
+        [100, TEXTURE_BUMPY],
+    ]
+
+    decisions = classify(to_classify)
+
+    for decision in decisions:
+        print("It's a(n):", LABELS[decision])
+
+
+if __name__ == '__main__':
+    main()