Hello everyone today is my first post and today I’m going to share about linear regression.

At its core, linear regression is a statistical method used for modeling the relationship between a dependent variable (also called the target or outcome) and one or more independent variables (predictors or features). The goal is to establish a linear relationship that allows us to predict the dependent variable based on the values of the independent variables.

The fundamental assumption of linear regression is that this relationship can be expressed by a straight line equation, hence the term “linear.” This equation can be represented as:

Y=β0​+β1​X+ε

Where:

• $Y$ is the dependent variable.
• $X$ is the independent variable.
• β0​ is the intercept.
• β1​ is the slope coefficient.
• $\epsilon$ represents the error term, accounting for the unexplained variability in the data.

The primary objective of linear regression is to find the best-fitting line that minimizes the sum of the squared differences between the observed and predicted values. This best-fitting line is often referred to as the “regression line” or “line of best fit.” It is determined by estimating the values of β0​ and β1​ that minimize the error term $\epsilon$.

To do this, we typically use a technique called “ordinary least squares” (OLS) regression, which finds the coefficients β0​ and β1​ that minimize the sum of the squared errors: