As we discussed Supervised and Unsupervised machine learning models in our previous chapter. If you did not go through it, please first read that chapter so it is feasible for you to understand this chapter about an algorithms of Supervised Machine Learning which is Linear Regression.

What is Regression in Machine Learning

Let discuss what is regression and why we have to use it mean discuss it purpose as well. So, Regression is a statistical method used as a supervised machine learning algorithms to predict a continuous value based on the relationship between one or more independent variables and a dependent variable. The primary goal is to estimate or predict the dependent variable using the independent variables.

For example: Imagine you have a dataset(container of data) containing information about various cars(each car is a sample for us), including their engine sizes, number of cylinders, fuel consumption, and CO2 emissions(these all are features). The goal is to predict the CO2 emission of a car based on its engine size, number of cylinders, etc.

Here CO2 emission is our dependent variable and engine size, number of cylinders are independent variables.

Types of variable in Regression in machine learning

So let’s discuss types of variable used in regression model for prediction of values.

In regression, there are two types of variables: dependent and independent. The dependent variable is the value we want to predict, while the independent variables are the factors that may influence or you can say effect the dependent variable.

Example in our case as I discussed above:
Dependent Variable (Y): CO2 Emission
Independent Variables (X): Engine Size, Number of Cylinders, Fuel Consumption etc

Types of Regression in machine learning

Basically The nature of the relationship between independent and dependent variables determines whether regression is linear or non-linear. There are two main types of regression: Simple Regression and Multiple Regression please don’t worry just overlook these type by given example below in next lectures we will briefly describe about these two types so don’t worry.

For Example:

Simple Linear Regression: Predicting CO2 Emission using Engine Size (assuming a linear relationship). Comparing 1 dependent and 1 independent variable like CO2 Emission & Engine Size respectively.

Multiple Linear Regression: Predicting CO2 Emission using Engine Size and Number of Cylinders (linear or non-linear relationship). Comparing 1 dependent and 1 or many independent variable like CO2 Emission & Engine Size and Number of Cylinders respectively.

Applications of Regression Analysis in Regression in machine learning

Regression analysis has applications in various fields where continuous value predictions are needed. lets see below examples for more clarity.

For example:

Sales Forecasting: Predicting a salesperson’s monthly/yearly(dependent variable) sales based on age, education, and experience(independent variable) etc.

Psychology: Estimating individual satisfaction(dependent variable) based on demographic and psychological factors(independent variable) etc.

Real Estate: Predicting house prices(dependent variable) based on size and number of bedrooms(independent variable) etc.

Employment Income: Estimating income(dependent variable) using variables like hours worked, education, and experience(independent variable) etc.

So as there are various application of regression where continuous value is our prediction as targeted value.

Conclusion

Now in last i want to conclude that, the text introduces the concept of regression, explains its key components like dependent and independent variables, discusses types of regression (simple and multiple), provides examples of regression applications in different fields.

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