Apply logistic regression to categorize whether a county had high mortality rate due to contamination

In [1]:

    

import pandas as pd
%matplotlib inline
import numpy as np
from sklearn.linear_model import LogisticRegression


    

In [5]:

    

cd C:\Users\Harsha Devulapalli\Desktop\algorithms\class7


    

    




C:\Users\Harsha Devulapalli\Desktop\algorithms\class7

In [6]:

    

df=pd.read_csv('data/hanford.csv')


    

In [13]:

    

len(df)


    

    
Out[13]:





9

In [7]:

    

df.describe()


    

    
Out[7]:






  

    

      

      
Exposure
      
Mortality
    
  
  

    

      
count
      
9.000000
      
9.000000
    
    

      
mean
      
4.617778
      
157.344444
    
    

      
std
      
3.491192
      
34.791346
    
    

      
min
      
1.250000
      
113.500000
    
    

      
25%
      
2.490000
      
130.100000
    
    

      
50%
      
3.410000
      
147.100000
    
    

      
75%
      
6.410000
      
177.900000
    
    

      
max
      
11.640000
      
210.300000
    
  

In [20]:

    

df['Mortality'].hist(bins=5)


    

    
Out[20]:





<matplotlib.axes._subplots.AxesSubplot at 0x226633b2160>






    

In [23]:

    

df['Mort_high']=df['Mortality'].apply(lambda x:1 if x>=147.1 else 0)
df['Exposure_high']=df['Exposure'].apply(lambda x:1 if x>=3.41 else 0)


    

In [25]:

    

df


    

    
Out[25]:






  

    

      

      
County
      
Exposure
      
Mortality
      
Mort_high
      
Exposure_high
    
  
  

    

      
0
      
Umatilla
      
2.49
      
147.1
      
1
      
0
    
    

      
1
      
Morrow
      
2.57
      
130.1
      
0
      
0
    
    

      
2
      
Gilliam
      
3.41
      
129.9
      
0
      
1
    
    

      
3
      
Sherman
      
1.25
      
113.5
      
0
      
0
    
    

      
4
      
Wasco
      
1.62
      
137.5
      
0
      
0
    
    

      
5
      
HoodRiver
      
3.83
      
162.3
      
1
      
1
    
    

      
6
      
Portland
      
11.64
      
207.5
      
1
      
1
    
    

      
7
      
Columbia
      
6.41
      
177.9
      
1
      
1
    
    

      
8
      
Clatsop
      
8.34
      
210.3
      
1
      
1
    
  

In [ ]:

    

df exposure_high(x):
    if x >=3.41
        return 1
    else:
        return 0
 # THIS IS THE FUNCTION ONE HAD TO USE IF IT WASNT FOR LAMBDA FUNCTION


    

In [15]:

    

from sklearn.linear_model import LogisticRegression


    

In [16]:

    

lm = LogisticRegression()


    

In [28]:

    

x = np.asarray(df[['Exposure_high']])
y = np.asarray(df['Mort_high'])


    

In [29]:

    

lm = lm.fit(x,y)


    

In [30]:

    

lm.predict([50])


    

    




c:\users\harsha devulapalli\appdata\local\programs\python\python35\lib\site-packages\sklearn\utils\validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
  DeprecationWarning)






    
Out[30]:





array([1], dtype=int64)