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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:

 

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.

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