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Pca example problems9/18/2023 ![]() ![]() PCA performs a linear transformation on the data so that most of the variance or information in your high-dimensional dataset is captured by the first few principal components. Dimensionality reduction is basically a technique where we reduce the number of columns or features from the dataset based on their relevance to the problem, the least their requirement, the more are the chances that they get removed from the dataset. While it is said that the more data we have, the more accurate results we are going to observe, while it is rightly said but it’s not just data that we require, we need high-quality data to get better results. ![]() It is also a great tool for exploratory data analysis for making predictive models. PCA is mainly used for dimensionality reduction in a dataset consisting of many variables that are highly correlated or lightly correlated with each other while retaining the variation present in the dataset up to a maximum extent. PCA is an unsupervised machine learning algorithm. What is Principal Component Analysis (PCA) ? Before proceeding here is a quick overview of what we cover in this post. The fundamental purpose of this post is to brief regarding the PCA algorithm step by step and in a way that everyone can easily understand what can actually PCA do and how we can use PCA in the project/algorithm. ![]()
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