Question

Predictive modeling uses statistics to predict outcomes through the use of models such as one of below:

Naive Bayes

k-nearest neighbor algorithm

Majority classifier

Support vector machines

Random forests

Boosted trees

Classification and Regression Trees (CART)

Multivariate adaptive regression splines (MARS)

Neural Networks

Ordinary Least Squares

Generalized Linear Models (GLM)

Logistic regression

Generalized additive models

Robust regression

Semiparametric regression

Choose one model to research further and then create a specific example/prototype of how it could be applied to a real-world problem.

Answer 1

Neural Networks

It is computational methods to develop predictive system simulating the behavior of neurons (basic unit of nervous system). It is one of the most advanced methods to build predictive intelligent models. Atrificial Neural networks keep learning with more use and their efficiency increases.

These are used in deep learning and machine learning.

One specific prototype how it can be applied to real life problem:

Taking knowledge base of criminal psychologists, educators, developmental psychologists, crime records and police department a neural network based machine learning model can be built and trained to learn patterns of criminal mind starting at early age, detect/ predict it in random samples, match with repeat offenders and predict probability of criminal traits for a person.