Codecademy export

script.py

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(12))

labels = flags[["Landmass"]]
#print(labels.head(10))

#data = flags[["Red", "Green", "Blue", "Gold", "White", "Black", "Orange"]]
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)

#tree = DecisionTreeClassifier(random_state = 1)
#tree.fit(train_data, train_labels)
#print(tree.score(test_data, test_labels))

scores = []
for i in range(1, 21):
  tree = DecisionTreeClassifier(random_state = 1, max_depth = i)
  tree.fit(train_data, train_labels)
  score = tree.score(test_data, test_labels)
  scores.append(score)
  #print(i, score)

plt.plot(range(1, 21), scores)
plt.show()