2. What is Predictive Analytics
It is used to make prediction about unknown future
events.
It uses many techniques from data mining, machine
learning, statistics, artificial intelligence.
5. Common Predictive Analytics Methods
Regression
Regression models predict a number – for example, how much revenue a customer
will generate over the next year or the number of months before a component will fail on a
machine.
Classification
Classification models predict class membership. For instance, if we try to classify
whether someone is likely to leave, whether he will respond to a thing, whether he’s a
good or bad credit risk, etc. Usually, the model results are in the form of 0 or 1, with 1
being the event we are targeting.
7. Linear Regression
It is one of the most widely used modeling technique.
In this technique, the dependent variable is continuous, independent
variable(s) can be continuous or discrete and nature of regression line is
linear.
Linear Regression establishes a relationship between dependent
variable (Y) and one or more independent variables (X) using a best
fit straight line (regression line).
10. Multiple Linear Regression
Multiple regression is an extension of simple linear regression.
It is used when we want to predict the value of a variable based on
the value of two or more other variables.
We could use multiple regression to understand whether daily
cigarette consumption can be predicted based on smoking duration,
age when started smoking, smoker type, income and gender.
