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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

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