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# What is Linear Regression in Machine Learning

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It helps us understand how different variables are related to each other.

The primary goal of linear regression is to find the best-fitting linear equation that describes the relationship between the dependent variable (Y) and the independent variable(s) (X).

The equation of a simple linear regression can be written as:

``Y = β0 + β1 * X + ε``

Where,

• Y is the dependent variable (the target or outcome variable).
• X is the independent variable (the predictor variable).
• β0 is the y-intercept, representing the value of Y when X is 0.
• β1 is the slope, representing the change in Y for a unit change in X.
• ε is the error term, representing the difference between the predicted value (Y) and the actual value (Y_actual).

The goal is to estimate the values of β0 and β1 that minimize the sum of squared errors between the predicted values and the actual values. This process is commonly done using a method called the Ordinary Least Squares (OLS) estimation.

There are two main types of linear regression:

1. Simple Linear Regression: In simple linear regression, there is only one independent variable (X) that is used to predict the dependent variable (Y). The relationship between Y and X is assumed to be linear.

2. Multiple Linear Regression: In multiple linear regression, there are two or more independent variables (X1, X2, X3, …, Xn) used to predict the dependent variable (Y). The relationship between Y and the multiple independent variables is assumed to be linear.

Linear regression is widely used in various fields, including