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How to Implement Multiple Linear Regression From Scratch

Implement Multivariate Linear Regression from scratch on python without using sklearn.

Linear regression is a type of machine learning algorithm which attempts to model the relationship between features and target variables by fitting a linear equation to the observed data.

The linear equation in multi-variate data is given by —

here, β are the different slopes for respective features and c is a constant (bias).

We load the necessary libraries and functions to perform —

Overview of the dataset.
Overview of the dataset

After loading the dataset, we have to preprocess the dataset. We will use the function StandardScaler to scale our data through the equation —

where μ is the mean, and σ is the standard deviation of the entire data.

The following code scales the data and divides them into training and testing samples.

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