# 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)
