Image from Pixabey What is PCA? Working with high-dimensional data is always a challenging task. In this modern technological era, we are more capable of capturing data in many aspects(variables) than ever. We can capture an instance with thousands of variables. Analyzing all these variables and finding each variable's effect(coefficients) on the target variable requires a huge computation power and electricity. We are not sure our built model is the best one even by doing so. Principal Component Analysis will address this very issue and provide us a solution, Principal Components. This blog discusses the Terminology and implementation of Principal Component Analysis. Overview of PCA : Principal Component Analysis is an Unsupervised Machine Learning technique used to reduce the dimensionality of the data by preserving the statistical information. Principal Component Analysis tries to find new variables i.e. Principal Components which are linear combinations of existin...
Image by Gerd Altmann from Pixabay " In Statistics we study the Chances or Probabilities of Occurrence or Happening of an Event or Phenomenon. In the world of Statistical Analysis presenting the results with 100% strong evident is impossible " Introduction In general gaining the information regarding the characteristics of whole population is practically not possible. It incurs a lot of money, time, labor and other constraints. So we take a sample from the population, study about it's characteristics and try to draw conclusions or to estimate the population characteristics from it. For example, A doctor records the Blood Pressure(BP) readings of 100 patients suffering with Hypertension and computes the average(Sample average) of those readings. Doctor uses this sample average information to draw conclusion about the average Blood Pressure (Population average) of whole patients who are suffering from Hypertension. Parameter and Statistic The Statistical characte...