What is Regression , Maths or Machine learning

Regression

In general, regression is defined as a technique to find the relation between two events/variables/activities.  

For Example:

• The relation between hard work done by students throughout the year to salary package they get during the campus interviews.
• The relation between the size of the house to the price of the house.

                  

If you remember your schooldays with 2-D coordinate geometry  (Thanks to S L Loney). He says the equation of a line is  "y=mx +c"

• y: Coordinate on Y-axis
• x: Coordinate of X-axis
• c: Intercept (where the line will intersect at Y-axis)
• M: Slope of a line. You can understand how much line is tilted towards X-axis. It is a number that measures lines "steepness" also called the gradient of a line.
• Slope of line is calculated as   (y2-y1/ x2-x1) , here (x1,y1) and (x2,y2) are two points on line in 2-dimension.

  

 So if you know x,m, and c you can find y .i.e y is dependent on the value of x, if x will change y will also change. "If you do more study, salary package will be more"

In statistical terms, Regression is a technique used to model and analyze the relationships between variables and oftentimes how they contribute and are related to producing a particular outcome together. Regression techniques are used when the output is real-valued based on continuous variables. For example, any time-series data. This technique involves fitting a line. Regression can also be thought of as looking for a trend line in our data. When used in machine learning, that line allows us to predict a numeric value based on features. We could use regression to predict things like:

·         How much money something will cost

·         What's the expected temperature tomorrow

·         How many days until you need to replace your car battery

It is a form of predictive modeling technique that investigates the relationship between a dependent (target) and the independent variable (s) (predictor). In the Regression problem, we try to predict results within a continuous output, i.e. we try to map input variables to some continuous function. (y=a+b*x)

Regression majorly depends on

• Number of independent variables
• Type of dependent variables
• The shape of the regression line

Types of Regression:

• Linear Regression
• Logistic Regression (it is considered as classification)
• Polynomial Regression
• Stepwise Regression
• Ridge Regression
• Lasso Regression
• Elastic Net Regression

Benefits of Regression:

• It indicates the significant relationships between the dependent variable and the independent variable.
• It indicates the strength of the impact of multiple independent variables on a dependent variable.

General Regression Problems:

• Given data about the size of houses on the real estate market, try to predict their price. Price as a function of size is a continuous output, so this is a regression problem
• Given a picture of a person, we have to predict their age on the basis of the given picture
• This technique is used for forecasting, time series modeling and finding the cause and  effect relationship