: Linear Regression is the most basic and widely used kind of
regression. A straight line is fitted to the data to represent the relationship between the
dependent variable and one or more independent variables. Linear regression finds
applications in various fields, such as finance (predicting stock prices based on historical
data), economics (analyzing the relationship between income and expenditure), and
marketing (predicting sales based on advertising spending).
: Logistic regression is used when the dependent variable is binary or
categorical. It estimates the probability of an event occurring based on the values of the
independent variables. Applications of logistic regression include medical research
(predicting the likelihood of disease occurrence), marketing (predicting customer churn),
and social sciences (predicting voting outcomes).
The link between the dependent variable and the independent
variable or variables is treated as a polynomial function in polynomial regression, which
is an extension of the linear regression. It is useful when the data exhibits a non-linear
pattern. Applications of polynomial regression include physics (modeling the trajectory
of a projectile), chemistry (predicting reaction rates based on temperature), and
environmental sciences (modeling population growth).
Ridge Regression and Lasso Regression
: Ridge and Lasso are variants of linear
regression that are used for dealing with multicollinearity and feature selection,
respectively. Ridge regression adds a penalty term to the linear regression objective
function, which helps in stabilizing the model when the independent variables are highly
correlated. Lasso regression, on the other hand, adds a penalty term that forces some