Principal Component Analysis

Introduction: Principal Component Analysis (PCA) is a powerful technique used in data science and machine learning for dimensionality reduction and exploratory data analysis. Despite its widespread application, understanding PCA can seem daunting to many. In this blog post, we'll embark on a journey to demystify PCA and explore its inner workings, applications, and benefits. As the number of dimensions increases, the number of possible combinations of features increases exponentially, which makes it computationally difficult to obtain a representative sample of the data and it becomes expensive to perform tasks such as clustering or classification because it becomes. Additionally, some  machine learning  algorithms can be sensitive to the number of dimensions, requiring more data to achieve the same level of accuracy as lower-dimensional data. To address the  curse of dimensionality ,  Feature engineering  techniques are used which include feature selection and ...