In [2]:

    

import pandas as pd
import numpy as np
from sklearn import tree
from sklearn.externals.six import StringIO
from IPython.display import Image
import pydotplus


    

In [14]:

    

df = pd.read_csv("../../data/homes_sf_ny/data.csv")


    

In [15]:

    

df.head()


    

    
Out[15]:






  

    

      

      
in_sf
      
beds
      
bath
      
price
      
year_built
      
sqft
      
price_per_sqft
      
elevation
    
  
  

    

      
0
      
0
      
2.0
      
1.0
      
999000
      
1960
      
1000
      
999
      
10
    
    

      
1
      
0
      
2.0
      
2.0
      
2750000
      
2006
      
1418
      
1939
      
0
    
    

      
2
      
0
      
2.0
      
2.0
      
1350000
      
1900
      
2150
      
628
      
9
    
    

      
3
      
0
      
1.0
      
1.0
      
629000
      
1903
      
500
      
1258
      
9
    
    

      
4
      
0
      
0.0
      
1.0
      
439000
      
1930
      
500
      
878
      
10
    
  

In [16]:

    

df['elevation'].median()


    

    
Out[16]:





18.5

In [ ]:

    

len(df)


    

In [ ]:

    

len(df[df['in_sf'] == 0]),len(df[df['in_sf'] == 1])


    

In [ ]:

    

df1 = df[(df['elevation'] <= 30) & (df['in_sf'] == 0) ]
df2 = df[(df['elevation'] <= 30) & (df['in_sf'] == 1) ]
df3 = df[(df['elevation'] > 30) & (df['in_sf'] == 0) ]
df4 = df[(df['elevation'] > 30) & (df['in_sf'] == 1) ]
len(df1),len(df2),len(df3),len(df4)


    

In [ ]:

    




    

In [3]:

    

f = open("../../data/homes_sf_ny/data.csv")
f.readline()
data = np.loadtxt(f,delimiter=",")
clf = tree.DecisionTreeClassifier()
Y = data[:,0]
X = data[:,1:]
clf = clf.fit(X,Y)


    

In [18]:

    

from IPython.display import Image  
dot_data = StringIO()  
tree.export_graphviz(clf, out_file=dot_data,  
                         feature_names=["beds","bath","price","year_built","sqft","price_per_sqft","elevation"],  
                         class_names=["NY","SF"],  
                         filled=True, rounded=True,  
                         special_characters=True)  
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  
Image(graph.create_png())


    

    
Out[18]:

In [22]:

    

clf.get_params


    

    
Out[22]:





<bound method DecisionTreeClassifier.get_params of DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            presort=False, random_state=None, splitter='best')>