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You have collected the 100's of parameters about the 1000's of websites e.g. daily hits, average time on the websites, number of unique visitors, number of returning visitors etc. Now you have find the most important parameters which can best describe a website, so which of the following technique you will use

APCA (Principal component analysis)

BLinear Regression

CLogistic Regression

DClustering
Principal component analysis . or PCA, is a technique for taking a dataset that is in the form of a set of tuples representing points in a high-dimensional space and finding the dimensions along which the tuples line up best. The idea is to treat the set of tuples as a matrix M and find the eigenvectors for MMT or M T M . The matrix of these eigenvectors can be thought of as a rigid rotation in a high-dimensional space. When you apply this transformation to the original data, the axis corresponding to the principal eigenvector is the one along which the points are most 'spread out,11 More precisely this axis is the one along which the variance of the data is maximized. Put another way, the points can best be viewed as lying along this axis, with small deviations from this axis.

You are asked to create a model to predict the total number of monthly subscribers for a specific magazine. You are provided with 1 year's worth of subscription and payment data, user demographic data, and 10 years worth of content of the magazine (articles and pictures). Which algorithm is the most appropriate for building a predictive model for subscribers?

ALinear regression

BLogistic regression

CDecision trees

DTF-IDF

You are using k-means clustering to classify heart patients for a hospital. You have chosen Patient Sex, Height, Weight, Age and Income as measures and have used 3 clusters. When you create a pair-wise plot of the clusters, you notice that there is significant overlap between the clusters. What should you do?

AIdentify additional measures to add to the analysis

BRemove one of the measures

CDecrease the number of clusters

DIncrease the number of clusters

Google Adwords studies the number of men, and women, clicking the advertisement on search

engine during the midnight for an hour each day.

Google find that the number of men that click can be modeled as a random variable with distribution

Poisson(X), and likewise the number of women that click as Poisson(Y).

What is likely to be the best model of the total number of advertisement clicks during the midnight for an hour ?

ABinomial(X+Y,X+Y)

BPoisson(X/Y)

CNormal(X+Y(M+Y)1/2)

DPoisson(X+Y)
The total number of clicks is the sum of the number of men and
women. The sum of two Poisson random variables also follows a Poisson distribution with
rate equal to the sum of their rates.
The Normal and Binomial distribution can approximate the Poisson distribution in
certain cases, but the expressions above do not approximate Poisson(X+Y).

ABinomial

BPoisson

CNormal

DAny of the above
In a binomial distribution, only 2 parameters, namely n and p, are needed to determine the probability. Where p is the probability of success and q is the probability of failure in a binomial trial, then the expected number of successes in n trials.
This is a binomial distribution because there are only 2 possible outcomes (we get a 5 or we don't).

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