Linear Regression

In [1]:

    

import pandas as pd
from sklearn.linear_model import LinearRegression


    

In [10]:

    

# https://github.com/scipy/scipy/issues/5998
import warnings
warnings.filterwarnings(action="ignore", module="scipy", message="^internal gelsd")


    

In [11]:

    

model = LinearRegression()


    

In [12]:

    

bmi_life_data = pd.read_csv("bmi_and_life_expectancy.csv")
model.fit(bmi_life_data[['BMI']], bmi_life_data[['Life expectancy']])


    

    
Out[12]:





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

In [13]:

    

laos_life_exp = model.predict(21.07931)


    

In [14]:

    

print(laos_life_exp)


    

    




[[ 60.31564716]]

In [15]:

    

from sklearn.datasets import load_boston


    

In [16]:

    

boston_data = load_boston()
x = boston_data['data']
y = boston_data['target']


    

In [17]:

    

house_model = LinearRegression()
house_model.fit(x, y)


    

    
Out[17]:





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

In [18]:

    

sample_house = [[2.29690000e-01, 0.00000000e+00, 1.05900000e+01, 0.00000000e+00, 4.89000000e-01,
                6.32600000e+00, 5.25000000e+01, 4.35490000e+00, 4.00000000e+00, 2.77000000e+02,
                1.86000000e+01, 3.94870000e+02, 1.09700000e+01]]
# TODO: Predict housing price for the sample_house
prediction = house_model.predict(sample_house)


    

In [19]:

    

print(prediction)


    

    




[ 23.68420569]