Multiple Linear Regression

Multiple Linear Regression is the type of regression in which more than one independent variables are used to predict the values of a dependent variable as opposed to Simple Linear regression where we have only a single independent variable(X).

Here we have X1, X2, X3, etc Which are the Independent variables or features.

For deeper understanding behind the mathematics of Linear Regression, use the following resources:

• Introduction to Linear Regression
• Linear Regression for Machine Learning

In this practise session, we will learn to code Multiple Linear Regression in 8 simple steps

We will perform the following steps to build a Multiple Linear Regressor using Beer dataset from How To Choose The Perfect Beer Hackathon.

Step 1. Data Preprocessing

• Importing the libraries.
• Importing the data set.
• Classifying dependent and independent variables.
• Creating training and test sets.

Step 2. Multiple Linear Regression

• Create a Multiple Linear Regressor.
• Training the regressor with training data.
• Predicting the salary for a test set.
• Calculating score from Root Mean Log Squared Error

Click on Start/Continue Hackathon to go to the practise page.