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 primary goal of using Principal Component Analysis in this session?

• To convert the dataset into a different format

• To eliminate all features from the dataset

• To find new principal components and reduce dimensionality

• To increase the number of features in the dataset

Why is it important to scale data before applying PCA?

• To remove all outliers from the dataset

• To increase the variance of the dataset

• To make sure all features have a standard deviation of 1 and a mean of 0

• To ensure all features have a mean of 100

What does the explained variance ratio indicate in PCA?

• The percentage of information each principal component retains from the original dataset

• The mean value of the dataset

• The number of features in the original dataset

• The total number of principal components

What library is used for implementing PCA in this session?

• TensorFlow

• Pandas

• SciKit Learn

• NumPy

What is a scree plot used for in PCA?

• To list all the eigenvectors

• To visualize the cumulative explained variance ratio

• To display the original dataset

• To show the mean and standard deviation of the dataset

How many principal components should be retained to preserve about 70% of the information?

• 4 principal components

• 6 principal components

• 10 principal components

• 16 principal components

What is the final step in the PCA process?

• Scaling the data again

• Transforming the dataset using the selected principal components

• Removing all principal components

• Adding new features to the dataset

Why should the target feature not be included in the PCA process?

• Because it will increase the number of principal components

• Because it will decrease the explained variance ratio

• Because it will make the dataset larger

• Because PCA is a feature extraction process, not a feature selection process

What is the main purpose of reducing dimensionality in PCA?

• To increase the complexity of the model

• To remove all data points

• To add more features to the dataset

• To simplify the dataset for further processing

What is the result of applying PCA to the dataset?

• A dataset with fewer features but retaining most of the original information

• A dataset with the same number of features

• A dataset with more features

• A dataset with no features