06. PCA Demo

Cd13639 C1 L2 DEMO 2 V1

Simplifying Complex Datasets with PCA

Learn about Principal Component Analysis (PCA), a method to simplify complex datasets:

  • PCA Purpose:
    • Reduces high-dimensional and complex datasets.
    • Combines highly correlated features.
  • Demonstration:
    • Uses an artificially generated two-feature dataset.
    • The second feature is derived from the first to highlight correlation.
    • Aims to reduce two features into one principal component.
  • Preparation Steps:
    • Import essential libraries: Pandas, Matplotlib, NumPy, and standard scaler for normalization.
    • Standard scaler normalizes data by computing the z-score.
  • Correlation Check:
    • High correlation is observed, both features move similarly.
  • PCA Process:
    • PCA reduces dataset complexity while capturing most information.
    • Apply the PCA function from scikit-learn.
    • Use fit and transform to convert data into PC representation.
  • Understanding Explained Variance:
    • Analyzes how much variation PCs explain.
    • First component captures significant variance (e.g., 95%).
    • Visualize variance via bar and cumulative charts.
  • Goal:
    • Achieve simplified datasets while preserving variances.
    • Target around 90% explained variance for effective complexity reduction.

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