ListenData: A Complete Guide to Linear Regression in Python

In this article we covered linear regression using Python in detail. It includes its meaning along with assumptions related to the linear regression technique. After completing this tutorial you will be able to test these assumptions as well as model development and validation in Python.

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Python : Linear Regression

Introduction to Linear Regression

Linear Regression is a supervised statistical technique where we try to estimate the dependent variable with a given set of independent variables. We assume the relationship to be linear and our dependent variable must be continuous in nature.
In the following diagram we can see that as horsepower increases mileage decreases thus we can think to fit linear regression. The red line is the fitted line of regression and the points denote the actual observations.

The vertical distance between the points and the fitted line (line of best fit) are called errors. The main idea is to fit this line of regression by minimizing the sum of squares of these errors. This is also known as principle of least squares.

Examples of Linear Regression

• Estimating the price (Y) of a house on the basis of its Area (X1), Number of bedrooms (X2), proximity to market (X3) etc.
• Estimating the mileage of a car (Y) on the basis of its displacement (X1), horsepower(X2), number of cylinders(X3), whether it is automatic or manual (X4) etc.
• To find the treatment cost or to predict the treatment cost on the basis of factors like age, weight, past medical history, or even if there are blood reports, we can use the information from the blood report.

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from Planet Python
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