The terms of interpolation and extrapolation are extremely important in anystatistical analysis. Extrapolation is the determination or estimation using aknown set of values or facts by extending it and taking it to an area or regionthat is unknown. It is the technique of inferring something using data that isavailable.
Interpolation on the other hand is the method of determining a certain valuewhich falls between a certain set of values or the sequence of values.This is especially useful when you have data at the two extremities of acertain region but you don’t have enough data points at the specific point.This is when you deploy interpolation to determine the value that you need.

The power analysis is a vital part of the experimental design. It is involved with the process of determining the sample size needed for detecting an effectof a given size from a cause with a certain degree of assurance. It lets youdeploy specific probability in a sample size constraint.
The various techniques of statistical power analysis and sample sizeestimation are widely deployed for making statistical judgment that areaccurate and evaluate the size needed for experimental effects in practice.Power analysis lets you understand the sample size estimate so that they areneither high nor low. A low sample size there will be no authentication toprovide reliable answers and if it is large there will be wastage of resources.

K-means clustering can be termed as the basic unsupervised learningalgorithm. It is the method of classifying data using a certain set of clusterscalled as K clusters. It is deployed for grouping data in order to find similarityin the data.
It includes defining the K centers, one each in a cluster. The clusters aredefined into K groups with K being predefined. The K points are selected atrandom as cluster centers. The objects are assigned to their nearest clustercenter. The objects within a cluster are as closely related to one another aspossible and differ as much as possible to the objects in other clusters. Kmeansclustering works very well for large sets of data.

Data Modeling: It can be considered as the first step towards the design of adatabase. Data modeling creates a conceptual model based on the relationshipbetween various data models. The process involves moving from theconceptual stage to the logical model to the physical schema. It involves thesystematic method of applying the data modeling techniques.
Database Design: This is the process of designing the database. The databasedesign creates an output which is a detailed data model of the database.Strictly speaking database design includes the detailed logical model of adatabase but it can also include physical design choices and storageparameters

n-dimensional vector of numerical features that represent some objectTerm occurrences frequencies, pixels of an image etc.Feature space: vector space associated with these vectors