Linear Regression is a fundamental concept in data science used for predicting continuous outcomes based on one or more input features. It does this by fitting a linear equation to observed data, connecting predictor variables (features) and a response variable (target).

In this lesson, we'll leverage Linear Regression to predict diamond prices using various features of diamonds (like carat, cut, color, etc.). Imagine a jeweler who wants to estimate the price of diamonds based on their attributes. Linear Regression can help by establishing a relationship between these features and the price.

Linear Regression is one of the simplest and most widely used techniques in machine learning and statistics for predicting a continuous outcome based on one or more input features. It aims to find a linear relationship between the predictor variables (features) and the response variable (target).

Here’s a step-by-step breakdown of what makes up a Linear Regression model:

1. Predictor and Response Variables: Linear Regression establishes a relationship where one or more features (predictor variables) are used to predict a target variable (response variable). For instance, in our diamond pricing example, features like carat, cut, color, and clarity are used to predict the price.

2. Linear Equation: The core of Linear Regression is the linear equation:

   $y={\beta }_0+{\beta }_1{x}_1+{\beta }_2{x}_2+\dots +{}_{}$