Linear Regression Made Simple: A Step-by-Step Tutorial

What is Linear Regression?

Linear Regression is a supervised learning algorithm in machine learning, which is widely used for solving regression problems. Regression is a type of machine learning problem where the goal is to predict a continuous output variable based on one or more input variables.

In Linear Regression, the goal is to find the best-fitting linear equation to describe the relationship between the input variables (also known as predictors or features) and the output variable (also known as the response variable).

The equation for a simple linear regression model can be written as follows:

y = b0 + b1 * x

Here, y is the dependent variable (the variable we are trying to predict), x is the independent variable (the predictor or feature), b0 is the intercept term (the value of y when x is zero), and b1 is the slope coefficient (the change in y for a unit change in x).

The goal of Linear Regression is to find the best values for b0 and b1 such that the line best fits the data points, minimizing the errors or the difference between the predicted values and the actual values.

Types of Linear Regression?

There are two main types of Linear Regression models: Simple Linear Regression and Multiple Linear Regression.

Simple Linear Regression: In simple linear regression, there is only one independent variable (also known as the predictor or feature) and one dependent variable (also known as the response variable). The goal of simple linear regression is to find the best-fitting line to describe the relationship between the independent and dependent variable. The equation for a simple linear regression model can be written as:

Y = b0 + b1 * X

Here, Y is the dependent variable, X is the independent variable, b0 is the intercept term, and b1 is the slope coefficient.

Multiple Linear Regression: In multiple linear regression, there are multiple…