Linear Regression Explained | A Beginner's Guide To Regression | The Basics You Need to Know!

Key Takeaways

linear regression is the simplest method used for regression meaning you aim to predict a continuous value simply put linear regression is a statistical method used to identify the linear relationship between a dependent variable Y and one or more independent variables X the dependent variable is the variable you want to predict while the independent variables are the variables you use for prediction for example estimating the house price of a house from its size number of bedrooms and whether it has air conditioning or not to understand linear regression let's refer to the linear functions Y = 2 * X is a straight line where for each one point increased in x value y increases by two this equation is a linear function the equation explains how the Y variable depends on X note that the equations of these forms are not linear so Y = 2 * X is the simplest example of a linear regression formula let's say Y is the price where one unit of Y is equal to $1,000 and X is the size of the house in square met so an increase of one square meter will increase the price of a house by $2,000 let's add the remaining features additionally linear functions have an intercept term in linear regression the coefficient is called the weight and The Intercept is called the bias bias can be interpreted as information that our features couldn't explain for example it may represent a default value for house prices if all our features are zero in practice the weights and the bias are estimated using a gradient descent optimization algorithm the cost function that explains the error of our model is often the mean squared error however there are still other common ones mean squared error compares the actual label with the predicted one and squares the error which both makes the value positive and mag sign ifies the larger errors let's visualize the learning procedure because we can't visualize four-dimensional feature space I will take square meters as the input feature and try to predict the house price the data set link can be found in the descriptions it has 545 observations from the plot it is easy to approximate the relationship of the variables which is probably linear let's run the optimization model the equation converges to this form now we can plug the size and estimate the price of a house that is not in the data set if you want to learn more about artificial intelligence subscribe to our channel to be aware of the new videos press the like button and let's discuss AI in the comments section