Linear Regression in Python
The code
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