machine_learning.principle_component_analysis
=============================================

.. py:module:: machine_learning.principle_component_analysis

.. autoapi-nested-parse::

   Principal Component Analysis (PCA) is a dimensionality reduction technique
   used in machine learning. It transforms high-dimensional data into a lower-dimensional
   representation while retaining as much variance as possible.

   This implementation follows best practices, including:
   - Standardizing the dataset.
   - Computing principal components using Singular Value Decomposition (SVD).
   - Returning transformed data and explained variance ratio.



Functions
---------

.. autoapisummary::

   machine_learning.principle_component_analysis.apply_pca
   machine_learning.principle_component_analysis.collect_dataset
   machine_learning.principle_component_analysis.main


Module Contents
---------------

.. py:function:: apply_pca(data_x: numpy.ndarray, n_components: int) -> tuple[numpy.ndarray, numpy.ndarray]

   Applies Principal Component Analysis (PCA) to reduce dimensionality.

   :param data_x: Original dataset (features)
   :param n_components: Number of principal components to retain
   :return: Tuple containing transformed dataset and explained variance ratio

   Example:
   >>> X, _ = collect_dataset()
   >>> transformed_X, variance = apply_pca(X, 2)
   >>> transformed_X.shape
   (150, 2)
   >>> len(variance) == 2
   True


.. py:function:: collect_dataset() -> tuple[numpy.ndarray, numpy.ndarray]

   Collects the dataset (Iris dataset) and returns feature matrix and target values.

   :return: Tuple containing feature matrix (X) and target labels (y)

   Example:
   >>> X, y = collect_dataset()
   >>> X.shape
   (150, 4)
   >>> y.shape
   (150,)


.. py:function:: main() -> None

   Driver function to execute PCA and display results.