A Visual Learner’s Guide to Explain, Implement and Interpret Principal Component Analysis (PCA)

High-dimensional data is a common issue experienced in machine learning practices, as we typically feed a large amount of features for model training. This results in the caveat of models having less interpretability and higher complexity — also known as the curse of dimensionality. PCA can be beneficial when the dataset is high-dimensional (i.e. contains many features) and it is widely applied for dimensionality reduction. Additionally, PCA is also used for discovering the hidden relationships among features and reveal underlying patterns that can be very insightful. PCA attempts to find linear components that capture as much variance in the data as possible, and the first principal component (PC1) is typically composed of features that contributes the most to model predictions. <a href="https://towardsdatascience.com/a-visual-learners-guide-to-explain-implement-and-interpret-principal-component-analysis-cc9b345b75be">Click Here</a>