Artificial Intelligence (AI) has recently gained popularity. People from various disciplines are attempting to use AI to make their jobs simpler. For example, AI is used by economists to forecast future consumer prices in order to benefit. In another example, we have physicians using AI to see if a tumor is benign or malignant. Yet another example is of meteorologists using AI/ML to predict the weather, or Human Resources (HR ) recruiters using it to review resumes and check if the applicant’s qualifications meet the minimum requirements of a job. ML algorithms are the driving force behind AI’s widespread adoption. A linear regression algorithm is a fundamental algorithm that any Machine Learning enthusiast begins with, and we’ll discuss more about Linear Regression here.

One of the most popular types of machine learning models is regression, which is used to estimate the relationships between variables. Regression in machine learning models estimates a numeric value, while classification models determine which group an observation belongs to.

Any machine learning problem involving continuous numbers, which includes a wide range of real-world applications, requires regression machine learning.

AI Linear Regression is a supervised machine learning algorithm with a continuous and constant slope expected performance. Rather than attempting to classify values into groups (such as cats and dogs), it is used to estimate values within a continuous range (such as price and revenue). There are two major categories:

- Simple Regression
- Multiple Regression

A type of linear regression for a machine learning algorithm known as simple linear regression models the relationship between a dependent variable and a single independent variable. A Simple Linear Regression model shows a linear or sloped straight-line relationship, which is why it is called Simple Linear Regression.

The dependent variable must be a continuous/real value, which is the most important aspect of Simple Linear Regression analysis. The independent variable, on the other hand, maybe calculated using either continuous or categorical values.

The key goals of the simple linear regression algorithm are:

Create a model that depicts the relationship between the two variables. Such as the income-to-expenditure ratio, experience-to-salary ratio, and so on.

New discoveries are being predicted. For example, weather forecasting based on temperature, company revenue based on annual expenditures, and so on.

We learned about SLR, or Simple Linear Regression previously, where a single Independent variable is used to model the response variable. However, there are several cases where more than one predictor variable affects the response variable; in these cases, the Multiple Linear Regression or MLR algorithm is used.

Furthermore, MLR is an extension of SLR in that it predicts the response variable using more than one predictor variable. It can be described as follows:

“*MLR (Multiple Linear Regression) is a common **regression analysis ai** algorithm that models the linear relationship between a single continuous dependent variable and multiple independent variables.”*

To finish off, here are some important assumptions of linear regression to keep in mind when creating such a model:

Linear relationship between features and target: Linear regression assumes that the dependent and independent variables have a linear relationship.

The term “multicollinearity” refers to a high degree of correlation between the independent variables. Due to multicollinearity, determining the true relationship between predictors and target variables can be difficult. Or, to put it another way, determining which predictor variable affects the target variable and which does not is difficult. As a result, the model assumes that the attributes or independent variables have little or no multicollinearity.

**Homoscedasticity:** Homoscedasticity occurs when the error term is the same for all values of independent variables. In a scatter plot with homoscedasticity, there should be no simple pattern distribution of results.

**Error term normal distribution:** The error term in linear regression is assumed to follow a normal distribution pattern. When error terms are not usually distributed, confidence intervals become either too large or too short, making finding coefficients difficult.

The q-q plot can be used to verify this. If the plot depicts a straight line with no deviations, the error is usually distributed.

**No autocorrelations:** In error terms, the linear regression model assumes no autocorrelation. If there is some association in the error term, the model’s accuracy would be significantly reduced. If there is a dependence between residual errors, autocorrelation is likely to occur.