Decision Tree

#Importing a library that is useful to read the original file
import pandas as pd
#Importing a visual library with some illustrative set up
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import matplotlib.colors as mcolors 
from matplotlib import cm
import math 
plt.style.use('fivethirtyeight')
plt.rcParams['font.family'] = 'sans-serif' 
plt.rcParams['font.serif'] = 'Ubuntu' 
plt.rcParams['font.monospace'] = 'Ubuntu Mono' 
plt.rcParams['font.size'] = 14 
plt.rcParams['axes.labelsize'] = 12 
plt.rcParams['axes.labelweight'] = 'bold' 
plt.rcParams['axes.titlesize'] = 12 
plt.rcParams['xtick.labelsize'] = 12 
plt.rcParams['ytick.labelsize'] = 12 
plt.rcParams['legend.fontsize'] = 12 
plt.rcParams['figure.titlesize'] = 12 
plt.rcParams['image.cmap'] = 'jet' 
plt.rcParams['image.interpolation'] = 'none' 
plt.rcParams['figure.figsize'] = (16, 8) 
plt.rcParams['lines.linewidth'] = 2 
plt.rcParams['lines.markersize'] = 8
colors = ['xkcd:pale orange', 'xkcd:sea blue', 'xkcd:pale red', 'xkcd:sage green', 'xkcd:terra cotta', 'xkcd:dull purple', 'xkcd:teal', 'xkcd: goldenrod', 'xkcd:cadet blue',
'xkcd:scarlet']
cmap_big = cm.get_cmap('Spectral', 512)
cmap = mcolors.ListedColormap(cmap_big(np.linspace(0.7, 0.95, 256))) 
bbox_props = dict(boxstyle="round,pad=0.3", fc=colors[0], alpha=.5)

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.tree import export_graphviz
from sklearn.externals.six import StringIO 
from IPython.display import Image 
from pydot import graph_from_dot_data
import pandas as pd
import numpy as np

data_original=pd.read_csv('/Users/Simone/Desktop/Programmi/DDA/hlsp_deep47tuc_hst_acs_47tuc_f606w-f814w_v1_catalog.txt',delimiter='\s+')
data_original.head()