What is linear regression in machine learning? 2025.

A return to machine learning

• Regression in machine learning helps us understand how different factors relate to each other and how they affect an outcome. Using algorithms, we can identify patterns in the data and make predictions for new situations using these patterns. For example, we can use regression to estimate how much a house will cost based on its size and location. It’s like learning from past experiences to better predict the future.

Types of regression models

There are basically three types of regression models which include:

1. linear regression
2. polynomial regression
3. Logistics regression

linear regression

• Linear regression in machine learning is a method used to understand and predict the relationship between two variables, these two variables are basically referred to as input variables (independent variables) and output variables (dependent variables). The goal of linear regression is to find a linear equation that best fits the data points, allowing us to make predictions about the output variables based on the input variables.

Let’s understand linear regression with a simple example to make it more clear:

• Imagine plotting points on a graph where one axis represents the input variable (like temperature) and the other axis represents the output variable (like ice cream sales) Linear regression helps us draw a line through these points that shows the general trend or pattern in the data. This line can be used to estimate the sales (output value) of ice cream based on temperature (input value).
• So, we can say that linear regression is a fundamental technique in machine learning to understand and predict linear relationships between variables. Consider this diagram and connect it to the example above, where temperature is an independent variable and ice cream is a dependent variable which is dependent on temperature. If the temperature increases, the sale of ice cream will also increase and vice versa.

A positive linear relationship

• In this type of linear relationship if the dependent variable on the Y axis increases, the independent variable on the X axis will also increase.

An example of this type of variable is shown in the figure above.

A negative linear relationship

• In this type of relationship, the dependent variable on the Y-axis decreases and the independent variable on the X-axis increases.

Below is an example for your reference.

stricken clan

• Gradient descent is a method used to find the best parameters for a linear equation fitting the data in linear regression. It is basically an optimization technique that iteratively adjusts the parameters of the linear equation to minimize the difference between the predicted and actual values, thereby helping us find the best-fitting line for our data.
• In simple terms, it’s like adjusting the slope and intercept of the line until it aligns with the actual data points.

The application of linear regression

Linear regression is a widely used technique in machine learning that enables us to make predictions and analyze past trends.

Some of the basic applications of linear regression are:

• Financial Forecasting – Analysts use linear regression to forecast stock prices, market trends, sales projections, and more.
• Risk Assessment – Insurance companies use linear regression to assess risk factors and predict insurance claims, premiums, and policyholder behavior.
• Customer Relationship Management (CRM) – Businesses use linear regression to analyze customer data, predict customer lifetime value, and personalize marketing strategies.

How to apply linear regression

1. To implement the linear regression model, you first need to implement some basic libraries in your PC, including:

• Import matplotlib. pyplot from plt
• Import Panda as a pd.
• Import numpy as np

1. In the next step, you need to define the data set in the program code, such as:

x= np. Array ([2.4, 5.0, 1.5, 3.8, 8.7, 3.6, 1.2, 8.1, 2.5, 5, 1.6, 1.6, 2.4, 3.9, 5.4])

y = np.array([2.1,4.7,1.7,3.6,8.7,3.2,1.0,8.0,2.4,6,1.1,1.3,2.4,3.9,4.8])

N = NP. Size (x)

1. In the third step, we have to define our X-axis and Y-axis and plot the defined data set accordingly.