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

1. Import the necessary packages to read in the data, plot, and create a logistic regression model


In [1]:
import pandas as pd
%matplotlib inline
import numpy as np
from sklearn.linear_model import LogisticRegression

2. Read in the hanford.csv file in the data/ folder


In [16]:
df = pd.read_csv("hanford.csv")
df.head()


Out[16]:
County Exposure Mortality
0 Umatilla 2.49 147.1
1 Morrow 2.57 130.1
2 Gilliam 3.41 129.9
3 Sherman 1.25 113.5
4 Wasco 1.62 137.5

3. Calculate the basic descriptive statistics on the data


In [11]:
df.describe()


Out[11]:
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

4. Find a reasonable threshold to say exposure is high and recode the data


In [15]:
df.std()


Out[15]:
Exposure      3.491192
Mortality    34.791346
dtype: float64

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exp_std = 3.491192

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5. Create a logistic regression model


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6. Predict whether the mortality rate (Cancer per 100,000 man years) will be high at an exposure level of 50


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