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● Non-linear Relationships: Regression is effective for modelling linear relationships but may not accurately
capture more complex, non-linear relationships. In such cases, techniques like polynomial regression or non-linear
regression might be more appropriate.
● Outliers: Outliers, or extreme data points, can disproportionately affect the regression model and lead to
inaccurate predictions. It’s crucial to assess the impact of outliers and consider alternative modelling approaches
if necessary.
● Violation of Assumptions: Regression analysis depends on certain assumptions, such as linearity of relationships
and absence of multicollinearity (high correlation between predictor variables). If these assumptions are violated,
the regression analysis results may be unreliable.
Linear regression is a supervised learning algorithm. It makes use of one independent variable, x, to predict the
outcome of a second dependent variable, y. This method finds the most accurate straight line that best describes the
relationship between the dependent and the independent variables, with minimum error.
Applications of Linear Regression
Linear regression is used in various Artificial Intelligence applications. It has its limitations, but its simplicity,
interpretability, and efficiency often exceed these limitations. Real life applications of linear regression include:
● Prediction of product demand
● Sales forecasting
● Analysing the effect of price change of a service
● Predict the effect of fertiliser on crop yield
● Prediction of revenue through advertisements
● Predicting salary of a person based on number of years of experience
Types of Linear Regression
There are two types of Linear Regression, which are as follows:
● Simple linear regression: It refers to the utilisation of a single independent variable for forecasting an outcome
of a numerical dependent variable.
y
x
● Multiple linear regression: It demonstrates a connection between two or more independent variables and the
associated variables that are dependent. The variables that are independent can be continuous or categorical.
This kind of regression type allows you to forecast patterns, predict potential outcomes, and forecast the effects
of adjustments.
y
x
Machine Learning Algorithms 331

