Linear Regression in Python

The code

Linear regression

In [1]:

from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_diabetes

In [2]:

X, y = load_diabetes(return_X_y=True)

In [5]:

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)

In [8]:

model = LinearRegression()

In [9]:

model.fit(X_train, y_train)

Out[9]:

LinearRegression()

In [11]:

model.predict(X_test)

Out[11]:

array([243.90286503, 255.12676628, 167.22172935, 118.47650203,
       192.14682945, 264.67907242, 113.22886906, 193.18930507,
       149.47596745, 243.20039294, 177.93174781, 175.74203763,
       110.46680777,  89.77086413, 244.02837746,  90.58763208,
       165.37154108,  63.14726802, 108.74614863, 219.73749355,
       207.13924127, 162.97419719, 167.60569831, 156.41465403,
       200.58885821, 168.12955047, 124.20284152,  82.87455833,
       197.35478449, 160.77717925, 181.97342322,  82.46207274,
       150.15573935, 147.98071862, 137.99282957, 199.82321561,
       167.52410859, 193.07125325, 127.68946283, 209.88171588,
        81.65517116, 169.84633658, 143.11666522, 187.05337125,
       176.99813504,  69.26632749, 144.64599866, 140.19327999,
       121.86233897, 243.8395608 , 166.98593103,  76.70158316,
       158.71811083, 156.25594867, 246.40084652, 178.50506066,
       189.8317059 , 121.59628799, 126.34365958, 173.54147058,
       219.4179752 , 174.29809566, 148.73031802, 109.07195518,
       262.15538729, 156.56991997,  81.12517751, 226.93291515,
       204.61510577,  43.05544664,  75.1271289 , 130.9296703 ,
       102.04574905, 139.88744784, 135.61051522, 196.77586003,
        93.44275224, 202.35753477, 218.25467264, 191.01610101,
       151.45583867, 213.48258586,  37.96942786, 212.46808471,
        79.46854302,  95.87890865, 146.41790237, 197.2868015 ,
       133.87473502, 151.79507785, 107.08044614, 125.99313955,
        74.40347743, 148.3429638 , 126.46953002, 105.91568339,
       242.52500525, 223.30558998, 122.36467682, 158.85253954,
       199.90320127, 117.77069021, 207.22526926,  83.0034864 ,
       217.87081927, 107.99156226, 218.46213017, 264.78236987,
       116.36779138, 112.52805271, 199.51252271, 124.92957722,
       171.12956185, 104.85905103,  97.38415651, 223.90597418,
       241.02116785, 115.85834592, 302.4180049 , 118.16008963,
       129.63886234, 112.8937251 ,  67.07287577, 248.66303619,
       104.71471792, 119.0531466 , 206.47038768, 186.46311712,
       194.40971714, 155.97717977, 192.10914578, 115.6170501 ,
       169.39983426, 155.89556534,  76.46598396, 178.77355727,
       267.07823317,  87.76242854,  78.22653687, 103.93066004,
        97.58767419, 249.18871246, 168.17689922,  90.57926686,
       110.9554089 , 105.83115494,  59.13838346, 105.94883305,
       154.71740324, 113.71308285, 199.35620081, 152.05718043,
       254.41145059, 213.56683551, 292.79083361, 104.28797617,
        96.74416823, 159.73684619, 147.7686736 , 179.4966634 ,
       231.19085444, 156.26410243, 103.74195179, 115.98304842,
        86.66315451, 173.93040978, 182.46755576, 196.1712543 ,
       106.02133405,  49.54377506, 164.81590271, 308.28976898,
       178.04688892, 154.01025939, 157.25300771, 179.14435465,
       156.3283682 ])

In [12]:

model.coef_

Out[12]:

array([ -76.47422556, -233.88380912,  554.45307405,  357.11086018,
       -710.94485909,  361.56418951,    8.08763807,  100.16057123,
        812.95456881,   40.24064777])

In [13]:

model.intercept_

Out[13]:

154.03968782873815

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