Implementing Multiple Linear Regression from Scratch

Introduction

Welcome to our exciting second class in the Regression and Gradient Descent series! In the previous lesson, we covered Simple Linear Regression . Now, we're transitioning toward Multiple Linear Regression, a powerful tool for examining the relationship between a dependent variable and several independent variables. Consider a case where we need to predict house prices, which undoubtedly depend on multiple factors, such as location, size, and the number of rooms. Multiple Linear Regression accounts for these simultaneous predictors. In today's lesson, you'll learn how to implement this concept in Python!

y = \beta_0 + \beta_1x

\mathbf{X} = \begin{bmatrix} 1 & x_{1,1} & x_{1,2} & \ldots & x_{1,m} \\ 1 & x_{2,1} & x_{2,2} & \ldots & x_{2,m} \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ 1 & x_{n,1} & x_{n,2} & \ldots & x_{n,m} \\ \end{bmatrix}

{x_{1}}

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