# Linear Regression

Term 1: Deep Learning Nanodegree Foundation

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In [1]:

import pandas as pd
from sklearn.linear_model import LinearRegression

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In [10]:

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

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In [11]:

model = LinearRegression()

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In [12]:

model.fit(bmi_life_data[['BMI']], bmi_life_data[['Life expectancy']])

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Out[12]:

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

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In [13]:

laos_life_exp = model.predict(21.07931)

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In [14]:

print(laos_life_exp)

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[[ 60.31564716]]

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## Multiple Linear Regression

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In [15]:

from sklearn.datasets import load_boston

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In [16]:

x = boston_data['data']
y = boston_data['target']

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In [17]:

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

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Out[17]:

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

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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)

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In [19]:

print(prediction)

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[ 23.68420569]

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