In [1]:

    

%matplotlib inline

import pandas as pd
import yellowbrick as yb
import matplotlib.pyplot as plt 

from sklearn.linear_model import LogisticRegression, Lasso


    

In [2]:

    

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


    

In [3]:

    

features = [
    "temperature", "relative humidity", "light", "C02", "humidity"
]
classes = ["unoccupied", "occupied"]


    

In [4]:

    

X = df[features]
y = df['occupancy']


    

In [5]:

    

la = Lasso()
la.fit(X,y)
print(la.coef_)


    

    




[-0.          0.          0.00170991  0.00015093  0.        ]

In [6]:

    

lr = LogisticRegression()
lr.fit(X,y)
print(lr.coef_)


    

    




[[-6.22110616e-01  1.06871188e-01  2.35735458e-02  3.42779586e-03
  -1.06125663e-05]]

In [7]:

    

print(lr.coef_.flatten())


    

    




[-6.22110616e-01  1.06871188e-01  2.35735458e-02  3.42779586e-03
 -1.06125663e-05]