Principal Component Analysis (PCA) is a dimensionality reduction technique used to simplify complex datasets while retaining most of their important information. It works by transforming the original variables into a new set of orthogonal variables called principal components. These components are ordered by the amount of variance they explain in the data, with the first component capturing the maximum variance, followed by the second, and so on.

Suppose we have a dataset with two features, height and weight, and we want to reduce it to one dimension using PCA. After standardizing the data, PCA calculates the covariance matrix and finds two eigenvectors. The eigenvector with the highest eigenvalue represents the direction of maximum variance in the data. PCA then projects the original data onto this eigenvector to obtain a one-dimensional representation of the dataset.

Key Features:

Data Loading and Preprocessing: Includes data loading, handling missing values, and data scaling.

PCA Implementation: Implements PCA using scikit-learn library to identify principal components.

Dimensionality Reduction: Reduces the dimensionality of the dataset while preserving most of the variance.