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Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.

The advantages of support vector machines are:

Effective in high dimensional spaces.

Still effective in cases where number of dimensions is greater than the number of

samples.

Uses a subset of training points in the decision function (called support vectors), so

it is also memory efficient.

Versatile: different Kernel functions can be specified for the decision function.

Common kernels are provided, but it is also possible to specify custom kernels.

The disadvantages of support vector machines include:

If the number of features is much greater than the number of samples, the method

is likely to give poor performances.

SVMs do not directly provide probability estimates, these are calculated using an

expensive five-fold cross-validation.

Sup Supervised learning tasks

Classification Regression

k-Nearest Neighbors Linear

Naive Bayes Locally weighted linear

Support vector machines Ridge

Decision trees Lasso

Unsupervised learning tasks Clustering Density estimation k-Means Expectation maximization DBSCAN Parzen window

When you are applying clustering technique and you find that there are quite a huge number of variables are available. Then it is better the find the co-relation among the variables and consider only one or two variables from the highly co-related variables. Because highly co-related variable will have the same effect, while creating the cluster. We can use scatter plot matrix among the variables to find the co-relation.

You can also combine several variables into a single variable. For example if you have two values in the dataset like Asset and Debt than by combining these two values like Debt to Asset ratio and use it while creating the cluster.

Principal component analysis (PCA) involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible: and each succeeding component accounts for as much of the remaining variability as possible.

Objectives of principal component analysis

1. To discover or to reduce the dimensionality of the data set.

2. To identify new meaningful underlying variables.

Point estimators

• minimum-variance mean-unbiased estimator (MVUE), minimizes the risk (expected loss) of the squared-error loss-function.

• best linear unbiased estimator (BLUE)

• minimum mean squared error (MMSE)

• median-unbiased estimator, minimizes the risk of the absolute-error loss function

• maximum likelihood (ML)

• method of moments, generalized method of moments

During the discovery phase you will be interviewing all the project stakeholders because they would be having quite a good amount of knowledge for the problem domain you will be working and you also interviewing project sponsors you will get to know what all are the expectations once project get completed. Hence, you will be noting down all the expectations from the project as well as you will be using their expertise in the domain.

Classification is the process of using several inputs to produce one or more outputs. For example the input might be the income, education and current debt of a customer The output might be a risk class, such as "good", "acceptable", "average", or "unacceptable". Contrast this to regression where the output is a number not a class.