PCA stands for Principal Component Analysis, which is a commonly used statistical technique in data analysis and machine learning.
PCA is a method of reducing the dimensionality of a dataset by identifying the underlying structure or patterns in the data. It works by transforming the original dataset into a new coordinate system that is aligned with the directions of maximum variance in the data. The transformed dataset, or principal components, are a set of linearly uncorrelated variables that explain the maximum amount of variance in the original dataset.
PCA can be used for a variety of tasks, such as data compression, feature extraction, and data visualization. It is particularly useful for datasets with a large number of variables or features, as it allows for the identification of the most important variables in the data and can simplify subsequent analysis. PCA is widely used in fields such as finance, biology, image processing, and many others.
- For English, copy the code in PCA_English.R
- For Bahasa Indonesia, copy the code in PCA_Indonesia.R
Data Used: data emiten.xlsx
How to use it: For each ## copy to R and check the result.