What is Predictive modeling?
- A set of methods to arrive at quantitative solution to problems of business interests
- It is a part of Data Science or Statistical learning
- Examples
- Predict whether a patient, hospitalized due to a heart attack, will have a second heart attack. The prediction is to be based on demographic, diet and clinical measurements for that patient.
- Predict the price of a stock in 6 months from now, on the basis of company performance measures and economic data.
- Identify the numbers in a handwritten ZIP code, from a digitized image.
- Estimate the amount of glucose in the blood of a diabetic person, from the infrared absorption spectrum of that person’s blood.
Predictive modeling process
Types of predictive model learning
The learning problems that we consider can be roughly categorized as either supervised or unsupervised
- Supervised learning
In supervised learning, the goal is to predict the value of an outcome measure based on a number of input measures
Examples
Predict whether a patient, hospitalized due to a heart attack, will have a second heart attack. The prediction is to be based on demographic, diet and clinical measurements for that patient.
Predict the price of a stock in 6 months from now, on the basis of company performance measures and economic data.
Identify the numbers in a handwritten ZIP code, from a digitized image.
Estimate the amount of glucose in the blood of a diabetic person, from the infrared absorption spectrum of that person’s blood.
- Unsupervised learning
In unsupervised learning, there is no outcome measure, and the goal is to describe the associations and patterns among a set of input measures.
Examples
Identifying the products that are usually sold together
Identifying of typical profile of employees who quit quickly
Variable Types and Terminology
- X-----> Set of inputs/Independent variables/Predictors
- Y------->Set of outputs/Dependent variables/Responses
