Data Science - PCA (Principal Component Analysis)
PCA (Principal Component Analysis) :
Principal Component Analysis is a statistical process that converts the observations of correlated features into a set of linearly uncorrelated features with the help of orthogonal transformation. These new transformed features are called the Principal Components. It is one of the popular tools that is used for exploratory data analysis and predictive modelling.
PCA works by considering the variance of each attribute because the high attribute shows the good split between the classes, and hence it reduces the dimensionality. Some real-world applications of PCA are image processing, movie recommendation system, optimizing the power allocation in various communication channels.
This assignment will study following statement :
Perform Principal component analysis and perform clustering using first 3 principal component scores both Heirarchial and K Means Clustering (scree plot or elbow curve)
and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data (class column we have ignored at the begining who shows it has 3 clusters)