# Principal component analysis

Principal Component Analysis (PCA) is a statistical technique used for dimensionality reduction and data visualization. It is commonly employed in various fields such as machine learning, data science, bioinformatics, and signal processing. The primary goal of PCA is to transform the original variables into a new set of uncorrelated variables, known as principal components, which capture the most significant patterns in the data.

How PCA Works:

- Standardize the Data: Often, the first step is to standardize the dataset so that each variable has a mean of zero and a standard deviation of one.
- Calculate the Covariance Matrix: The covariance matrix captures the relationships between variables in the dataset.
- Compute Eigenvalues and Eigenvectors: The eigenvalues and eigenvectors of the covariance matrix are computed. The eigenvectors represent the directions of maximum variance, and the eigenvalues indicate the magnitude of the variance in those directions.
- Sort Eigenvalues and Eigenvectors: The eigenvalues are sorted in descending order, and the corresponding eigenvectors are also arranged accordingly.
- Select Principal Components: The top \(k\) eigenvectors are chosen as the principal components, where \(k\) is the number of dimensions to which you want to reduce the data.
- Transform Original Data: The original data is then projected onto the selected principal components to obtain the reduced-dimensionality dataset.

Advantages of PCA:

- Dimensionality Reduction: PCA helps in reducing the number of variables while retaining most of the original variance, which is useful for visualization and computational efficiency.
- Noise Reduction: By focusing on the components with the highest variance, PCA can help filter out noise in the data.
- Uncovering Hidden Patterns: PCA can reveal relationships between variables that were not initially apparent.

Limitations of PCA:

- Linearity: PCA assumes that the data's underlying structure is linear, which may not be the case for all datasets.
- Loss of Interpretability: The principal components are linear combinations of the original variables, which may make them less interpretable.
- Sensitivity to Outliers: PCA is sensitive to outliers, which can distort the principal components.