Multiple Linear Regression
Multiple Linear Regression is the type of regression in which more than one independent variable 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 practice 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 the 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 practice page.