Practical Data Science using Python - Principal Component Analysis Practical

The video tutorial covers the practical application of Principal Component Analysis (PCA) using Python. It begins with an introduction to PCA and the dataset used, followed by data scaling with Standard Scaler. The tutorial then demonstrates applying PCA to the transformed data, explaining the concept of explained variance and the use of a scree plot. It discusses dimensionality reduction by selecting principal components and concludes with transforming the data using the chosen components for further analysis.

What is the role of the covariance matrix in PCA?

How does PCA help in reducing the dimensionality of a dataset?

What is the final output of the PCA process as described in the text?

What should you ensure regarding the target feature when applying PCA?