Principal Component Analysis (PCA) Explained | Dimensionality Reduction with Python

What do we do when a dataset contains many variables, strong correlations and more dimensions than we really need? Principal Component Analysis (PCA) provides one approach to dimensionality reduction. In this practical Data Science class, we break PCA down from the intuition behind variance and correlated features to implementing dimensionality reduction using Python. You will learn: • What Dimensionality Reduction means • Why high-dimensional data can be challenging • Correlated Features • Principal Components • Variance and Covariance • Eigenvalues and Eigenvectors • Feature Standardization • Explained Variance • Cumulative Explained Variance • Scree Plots • Component Loadings • PCA Transformation • PCA with Machine Learning Pipelines We use visual examples to explain how PCA identifies new directions in the data that capture as much variation as possible. The practical Python section demonstrates how to standardize features, fit PCA using scikit-learn, examine explained_variance_ratio_, choose the number of components and transform a high-dimensional dataset. We also discuss an important trade-off: Reducing dimensions can simplify a dataset, but principal components are often less directly interpretable than the original variables. PCA is widely applied in Data Science, Finance, Insurance, Risk Modelling, image processing, customer analytics and Machine Learning preprocessing. Subscribe to The Infinite Actuary for practical Data Science, Machine Learning, Actuarial Science, Analytics and Risk classes. Learn the concept. Build the skill. Apply it. #PCA #DimensionalityReduction #DataScience #MachineLearning #TheInfiniteActuary