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 specific example/prototype of how it could be applied to a real-world problem.

2. (15 points) Buses arrive at a bus stop following a Poisson process with rate 4 per hour. Also, empty taxis pass by according to a Poisson process with rate 10 per hour. You just arrived at the bus stop. Since you are in a hurry, you decided to ride the second taxi passing by if the next bus does not arrive by then. Find the probability that you will eventually ride a taxi.PRoBLEM 10.4 A chemical solution contains N molecules of type A and an equal number of molecules of type B. A reversible reaction occurs between type A and B molecules in which they bond to form a new compound AB. Suppose that in any small time interval of length h, any particular unbonded A molecule will react with any particular unbonded B molecule with probability och + O[h), where or is a reaction rate of formation. Suppose also that in any small time interval of length hI any particular AB molecule disassociates into its A and B constituents with probability ll + DUI), where fl is a reaction rate of dissolution. Let X (t) denote the number of AB molecules at time t. Model X(t} as a birth and death process by specifying the parameters. Note that one mole of molecules is N = 6.02214129 X 1023. 9.134. Let N(t) be the Poisson process, and suppose we form the phase-modulated process Y(t) = a cos(2Tift + TN(t)). (a) Plot a sample function of Y(t) corresponding to a typical sample function of N(t). (b) Find the joint density function of Y(1) and Y(t2). Hint: Use the independent incre- ments property of N(t). (c) Find the mean and autocorrelation functions of Y(t). (d) Is Y(t) a stationary, wide-sense stationary, or cyclostationary random process? (e) Is Y(t) mean square continuous? (f) Does Y(t) have a mean square derivative? If so, find its mean and autocorrelation functions