Dimension is a critical component of data science. Data has a number of dimensions. The dimensions are denoted by the letter n.
Assume you're a data scientist working in a financial company and you have to deal with customer data that includes their credit score, personal information, salary, and hundreds of other variables.
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Dimensionality reduction is used to understand the significant labels that contribute to our model. A reduction algorithm is something like PCA.
We can use PCA to reduce the number of dimensions while keeping all of the important ones in our model. There are PCAs for each dimension, and each one is perpendicular to the other (or orthogonal). The dot product of all the orthogonal PCs is 0.
