Machine Learning : Linear Regression |Dileenet A/L ICT

What is Linear Regression?

Linear Regression is an algorithm that comes under the Supervised Learning model.

What is Regression?

 Regression is a method of setting a target value based on independent predictors.

 Linear Regression shows the relationship between the independent variable and the dependent variable.

 Let's look at an example

 Below is the average number of hours a student studies and the marks obtained accordingly. This is a simple dataset

Study Hour (x) is the independent variable and test score (y) is the dependent variable. Now let's plot this on a graph

Linear Regression is about finding the best fit Linear Regression Line based on the data points in the graph above.

Now we can use the graph above to determine the number of marks a student will get based on the number of hours they study.

 Let's see how to find it.

 Since this is a linear graph, we can get an equation

 y = mx + c

 Here, y is the dependent variable, which is the Test Score. Here, X is the study hour, which is the independent variable.

 

When we build this according to the dataset, we can find a reasonable straight line. That means we get two values, m and c.

 Now we can see approximately how many marks a student will get when given a study hour.

 Now, after training this machine learning model using this dataset, we can find m and c.

 Accordingly, the number of hours a student studies can be used to predict the score he will get. This is called Linear Regression.

 We will talk about how to do this using python in the next post.

Previous post

https://e-learnict.blogspot.com/2026/01/dogs-cats-and-data-understanding.html

https://e-learnict.blogspot.com/2026/01/machine-learning-supervised-vs.html

https://e-learnict.blogspot.com/2026/01/introduction-to-reinforcement-learning.html

#LinearRegression

#Machinelearning

#supervicedlearning

#dataset