Practical Data Analysis Cookbook

Building Linear Regression model

Linear regression is arguably the simplest model that one can build. It is a model of choice if you know that the relationship between your dependent variable and independent ones is linear.

Getting ready

To execute this recipe, you will need pandas, NumPy, and Scikit. No other prerequisites are required.

How to do it…

Estimating the regression model with Scikit is extremely easy (the regression_linear.py file):

import sys sys.path.append('..') # the rest of the imports import helper as hlp import pandas as pd import numpy as np import sklearn.linear_model as lm @hlp.timeit def regression_linear(x,y): ''' Estimate a linear regression ''' # create the regressor object linear = lm.LinearRegression(fit_intercept=True, ...