In [1]:

    

# loading the libraries
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
import os
import matplotlib.pylab as plt
from sklearn.cross_validation import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import classification_report
import sklearn.metrics
 # Feature Importance
from sklearn import datasets
from sklearn.ensemble import ExtraTreesClassifier


    

In [2]:

    

#Load the dataset
gapdata= pd.read_csv("gap.csv", low_memory=False)
data_clean=gapdata.dropna()


    

In [3]:

    

# Data pre-processing tasks
data_clean['breastcancerper100th']= data_clean['breastcancerper100th'].convert_objects(convert_numeric=True)
data_clean['femaleemployrate']= data_clean['femaleemployrate'].convert_objects(convert_numeric=True)
data_clean['alcconsumption']= data_clean['alcconsumption'].convert_objects(convert_numeric=True)


    

    




C:\Anaconda2\lib\site-packages\ipykernel\__main__.py:1: FutureWarning: convert_objects is deprecated.  Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.
  if __name__ == '__main__':
C:\Anaconda2\lib\site-packages\ipykernel\__main__.py:2: FutureWarning: convert_objects is deprecated.  Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.
  from ipykernel import kernelapp as app
C:\Anaconda2\lib\site-packages\ipykernel\__main__.py:3: FutureWarning: convert_objects is deprecated.  Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.
  app.launch_new_instance()

In [4]:

    

#Create binary Breast Cancer Rate
def bin2cancer (row):
   if row['breastcancerper100th'] <= 20 :
      return 0
   elif row['breastcancerper100th'] > 20 :
      return 1

#Create binary Alcohol consumption
def bin2alcohol(row):
   if row['alcconsumption'] <= 5 :
      return 0
   elif row['alcconsumption'] > 5 :
      return 1
# create binary Female employee rate
def bin2femalemployee(row):
   if row['femaleemployrate'] <= 50 :
      return 0
   elif row['femaleemployrate'] > 50 :
      return 1
#Apply the new variables bin2alcohol,bin2femalemployee, bin2cancer to the gapmind dataset
data_clean['bin2femalemployee'] = data_clean.apply (lambda row: bin2femalemployee (row),axis=1)
data_clean['bin2alcohol'] = data_clean.apply (lambda row: bin2alcohol (row),axis=1)
data_clean['bin2cancer']=data_clean.apply(lambda row: bin2cancer(row),axis=1)


    

In [5]:

    

data_clean.dtypes


    

    
Out[5]:





alcconsumption          int64
breastcancerper100th    int64
femaleemployrate        int64
bin2femalemployee       int64
bin2alcohol             int64
bin2cancer              int64
dtype: object

In [6]:

    

data_clean.describe()


    

    
Out[6]:






  

    

      

      
alcconsumption
      
breastcancerper100th
      
femaleemployrate
      
bin2femalemployee
      
bin2alcohol
      
bin2cancer
    
  
  

    

      
count
      
165.000000
      
165.000000
      
165.000000
      
165.000000
      
165.000000
      
165.000000
    
    

      
mean
      
6.672727
      
37.521212
      
47.951515
      
0.424242
      
0.539394
      
0.751515
    
    

      
std
      
4.985545
      
23.038035
      
14.789089
      
0.495732
      
0.499963
      
0.433450
    
    

      
min
      
0.000000
      
4.000000
      
12.000000
      
0.000000
      
0.000000
      
0.000000
    
    

      
25%
      
3.000000
      
21.000000
      
39.000000
      
0.000000
      
0.000000
      
1.000000
    
    

      
50%
      
6.000000
      
30.000000
      
48.000000
      
0.000000
      
1.000000
      
1.000000
    
    

      
75%
      
10.000000
      
50.000000
      
56.000000
      
1.000000
      
1.000000
      
1.000000
    
    

      
max
      
23.000000
      
101.000000
      
83.000000
      
1.000000
      
1.000000
      
1.000000
    
  

In [7]:

    

# Assign predictor and traget variable
predictors=data_clean[['bin2alcohol','bin2femalemployee']]
target=data_clean.bin2cancer


    

In [8]:

    

#Split into training and testing sets
pred_train,pred_test,tar_train,tar_test=train_test_split(predictors,target,test_size=0.4)


    

In [9]:

    

pred_train.shape


    

    
Out[9]:





(99, 2)

In [33]:

    

#tar_test.head


    

In [10]:

    

pred_test.shape


    

    
Out[10]:





(66, 2)

In [11]:

    

tar_train.shape


    

    
Out[11]:





(99L,)

In [12]:

    

tar_test.shape


    

    
Out[12]:





(66L,)

In [24]:

    

#Build model on training data
from sklearn.ensemble import RandomForestClassifier
classifier=DecisionTreeClassifier()
classifier=classifier.fit(pred_train,tar_train)
predictions=classifier.predict(pred_test)


    

In [25]:

    

sklearn.metrics.confusion_matrix(tar_test,predictions)


    

    
Out[25]:





array([[ 8,  7],
       [ 4, 47]])

In [26]:

    

sklearn.metrics.accuracy_score(tar_test, predictions)


    

    
Out[26]:





0.83333333333333337

In [27]:

    

# fit an Extra Trees model to the data
model = ExtraTreesClassifier()
model.fit(pred_train,tar_train)


    

    
Out[27]:





ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',
           max_depth=None, max_features='auto', max_leaf_nodes=None,
           min_samples_leaf=1, min_samples_split=2,
           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
           oob_score=False, random_state=None, verbose=0, warm_start=False)

In [22]:

    

# display the relative importance of each attribute
print(model.feature_importances_)


    

    




[ 0.49408698  0.50591302]

In [28]:

    

"""
Running a different number of trees and see the effect
 of that on the accuracy of the prediction
"""
trees=range(25)
accuracy=np.zeros(25)
for idx in range(len(trees)):
   classifier=RandomForestClassifier(n_estimators=idx + 1)
   classifier=classifier.fit(pred_train,tar_train)
   predictions=classifier.predict(pred_test)
   accuracy[idx]=sklearn.metrics.accuracy_score(tar_test, predictions)


    

In [29]:

    

%matplotlib inline
plt.cla()
plt.plot(trees, accuracy)


    

    
Out[29]:





[<matplotlib.lines.Line2D at 0xaa53ef0>]






    

In [ ]: