Title: Linear Regression Using Scikit-Learn
Slug: linear_regression_using_scikit-learn
Summary: How to conduct linear regression in scikit-learn for machine learning in Python.
Date: 2017-09-18 12:00
Category: Machine Learning
Tags: Linear Regression
Authors: Chris Albon

Preliminaries



In [6]:

    
# Load libraries
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston
import warnings

# Suppress Warning
warnings.filterwarnings(action="ignore", module="scipy", message="^internal gelsd")

Load Boston Housing Dataset



In [7]:

    
# Load data
boston = load_boston()
X = boston.data
y = boston.target

Fit A Linear Regression



In [8]:

    
# Create linear regression
regr = LinearRegression()

# Fit the linear regression
model = regr.fit(X, y)

View Intercept Term



In [9]:

    
# View the intercept
model.intercept_









    Out[9]:





36.491103280361038

View Coefficients



In [10]:

    
# View the feature coefficients
model.coef_









    Out[10]:





array([ -1.07170557e-01,   4.63952195e-02,   2.08602395e-02,
         2.68856140e+00,  -1.77957587e+01,   3.80475246e+00,
         7.51061703e-04,  -1.47575880e+00,   3.05655038e-01,
        -1.23293463e-02,  -9.53463555e-01,   9.39251272e-03,
        -5.25466633e-01])