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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.

Hackathon Reviews


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  1. Multiple Linear Regression


    got to know when to use Root Mean Squared Logistic Error and what is the advantage of calculating RMSLE, like finding error and score of the model to improvise the model.
    and the boon of RMSLE over RMSE.

  2. Intuitive


    This interactive practice interface with concise write-up of the underlying theory and concepts is a good way to get started and sustain the motivation.
    Personally, this is a good refresher.

    Thank you.


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