

In [1]:

    
import seaborn as sns
iris = sns.load_dataset("iris")
iris.head()









    Out[1]:







  
    
      
      sepal_length
      sepal_width
      petal_length
      petal_width
      species
    
  
  
    
      0
      5.1
      3.5
      1.4
      0.2
      setosa
    
    
      1
      4.9
      3.0
      1.4
      0.2
      setosa
    
    
      2
      4.7
      3.2
      1.3
      0.2
      setosa
    
    
      3
      4.6
      3.1
      1.5
      0.2
      setosa
    
    
      4
      5.0
      3.6
      1.4
      0.2
      setosa



In [2]:

    
%matplotlib inline
import seaborn as sns; sns.set()
sns.pairplot(iris, hue="species", size = 1.5);

This is a header



In [3]:

    
X_iris = iris.drop("species", axis=1)
X_iris.shape









    Out[3]:





(150, 4)



In [4]:

    
y_iris = iris['species']
y_iris.shape









    Out[4]:





(150,)

Sci kit learn's Estimator Api



In [5]:

    
import matplotlib.pyplot as plt
import numpy as np

rng = np.random.RandomState(42)
x = 10*rng.rand(50)
y = 2*x-1+rng.randn(50)
plt.scatter(x,y)









    Out[5]:





<matplotlib.collections.PathCollection at 0x7f6dbc5320d0>



In [7]:

    
from sklearn.linear_model import LinearRegression
model = LinearRegression(fit_intercept=True)
model









    Out[7]:





LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)



In [15]:

    
X = x[:, np.newaxis]
X.shape









    Out[15]:





(50, 1)



In [16]:

    
model.fit(X,y)









    Out[16]:





LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)



In [ ]:



In [ ]:

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa