adding all work done so far (lessons 1 - 5)

python/Supervised Learning/Decision Trees/18. Decision Trees in sklearn/quiz.py

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+# Import statements
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import accuracy_score
+import pandas as pd
+import numpy as np
+
+# Read the data.
+data = np.asarray(pd.read_csv('data.csv', header=None))
+# Assign the features to the variable X, and the labels to the variable y.
+X = data[:, 0:2]
+y = data[:, 2]
+
+
+# TODO: Create the decision tree model and assign it to the variable model.
+# You won't need to, but if you'd like, play with hyperparameters such
+# as max_depth and min_samples_leaf and see what they do to the decision
+# boundary.
+model = DecisionTreeClassifier(max_depth=7, min_samples_leaf=10)
+
+# TODO: Fit the model.
+model.fit(X, y)
+
+# TODO: Make predictions. Store them in the variable y_pred.
+y_pred = model.predict(X)
+print(y_pred)
+
+# TODO: Calculate the accuracy and assign it to the variable acc.
+acc = accuracy_score(y, y_pred)
+print(acc)