import codecademylib3_seaborn
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt

flags = pd.read_csv("flags.csv", header = 0)
#print(flags.head())

labels = flags[["Landmass"]]

data = flags[["Red","Green", "Blue","Gold","White","Black", "Orange","Circles", "Crosses","Saltires","Quarters","Sunstars",
"Crescent","Triangle"]]

train_data, test_data, train_labels, test_labels = train_test_split(data, labels, random_state=1)

scores = []

for i in range(1, 20):
  tree = DecisionTreeClassifier(random_state=1, max_depth=i)
  tree.fit(train_data, train_labels)
  score = tree.score(test_data, test_labels)
  scores.append(score)
  
plt.plot(range(1,20), scores)
plt.show()


