In machine learning, linear regression enables you to discover patterns and relationships in data and come to a well-informed conclusion or forecast.

When you learn machine learning, Linear Regression is perhaps the most straightforward and well-liked method you’ll come across. With the help of this technique, you may simulate a linear relationship between the features in your data. Let’s explore this in greater detail: You need the information to create ML models that develop novel solutions to issues. Many of them. So, you must carefully examine the scant information available. To assist you in creating an informed choice or prediction with your model, you look for patterns and relationships between its features. This is where machine learning’s use of linear regression comes into play.

In this blog, we will cover the following sections:

- Describe regression
- Linear Regression Types
- What is a Linear Regression?
- Endnotes

**Describe Regression**

A regression statistical method demonstrates an algebraic link between two or more variables. One may predict the value of a variable given the values of the other variables based on this algebraic relationship.

Correlation is frequently used to determine whether there is a relationship between two variables. Regression is utilized to determine its strength and potential for prediction if a relationship is discovered.

**For Example:**

- Predict monthly rainfall in centimetres
- Make a day-ahead stock price prediction

As you now have a basic understanding of regression, let’s look at the many forms of regressions.

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**Types of Regressions**

- Linear regression
- Logistic regression
- Polynomial regression
- Stepwise regression
- Ridge regression
- Lasso regression
- ElasticNet regression

**What is a Linear Regression?**

One of the most fundamental machine learning algorithms, or supervised learning methods, is linear regression, which you can learn in an **ML course**. In essence, it executes a regression task. Based on independent factors, regression models forecast a dependent value. It is mostly used to determine how variables and forecasting relate to one another. Regression models vary according to the number of independent variables they use as well as the type of relationship they take into account between the dependent and independent variables.

In order to forecast the value of a dependent variable (y) based on a given independent variable, linear regression does the task. As a result, this regression approach determines a linear courting among x (input) and y (output). Thus, the term “linear regression” became coined.

Linear Regression further divided into

- Simple Linear Regression/Univariate Linear Regression
- Multivariate Linear Regression

**Endnotes**

A straightforward yet effective supervised machine learning algorithm is linear regression. There are various institutes that offer machine learning certification** **courses, which you can join. One of the first models a machine learning enthusiast should learn is this one. Machine learning is a rapidly expanding field that has been embraced by a number of IT industry sectors and has a significant global impact on large enterprises.