Earlier, I wrote about how to implement a simple linear regression (SLR) model in python. SLR is probably the easiest model to implement among the most popular machine learning algorithms. In this post, we are going to take it one step further and instead of working with just one independent variable, we will be working with multiple independent variables. Such a model is called a multiple linear regression (MLR) model.
How does the model work?
A multiple linear model can be described by a linear equation consisting of multiple independent variables.
For example:
In this equation, ß (beta) defines all the coefficients, x defines all the independent variables and y defines dependent variable.
An SLR model is a simplified version of an MLR model where there is only one x. Linear regression models use a technique called Ordinary Least Squares (OLS) to find the optimum value for the betas. OLS consists of calculating the error which is the difference between predicted value and actual value and then taking square of it. The goal is to find the betas that minimize the sum of the squared errors.
If you want to learn more about SLM and OLS, I highly recommend this visual explanation.
How can I implement MLR model?
Implementing an MLR model is pretty much the same as implementing an SLR model. The only difference is that because you have additional independent variables, you need to make sure you select only the most relevant features/independent varibles in your final model. Not all of your data is necessary to build your model. In fact, most of the times, a lot of your data will be garbage and as you know garbige in means garbage out! The process of refining your model by selecting ideal features is called ‘feature selection’. I will not be covering that in this post but will definitely do in a separate post.
As discussed before, we are going to be following these steps:
- Exploring the dataset
- Preprocessing the dataset
- Splitting the dataset into training and testing set
- Building the model
- Evaluating the model
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Hope you liked this post and let me know your thoughts and your suggestions!
You can download this code from my github.
this is multiple regression, not multivariate regression….
Thank you and apologies for the mistake. I have updated the post.