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(A) To perform regression analysis
(B) To cluster data into groups
(C) To reduce dimensionality
(D) To classify data points based on the majority class among their K nearest neighbors

(A) To increase model complexity
(B) To add more features to the dataset
(C) To bring all features to a similar scale for better model performance
(D) To remove irrelevant features

(A) Feature scaling
(B) One-hot encoding
(C) Data normalization
(D) Text vectorization

(A) Application Programming Interface
(B) Advanced Programming Integration
(C) Algorithmic Programming Interface
(D) Automated Program Integration

(A) To visualize data
(B) To summarize data
(C) To build predictive models
(D) To determine if there is a significant difference or effect

(A) Descriptive statistics
(B) Exploratory data analysis
(C) Inferential statistics
(D) Data preprocessing

(A) Data transformation
(B) Data cleaning
(C) Data aggregation
(D) Data transformation

(A) Encode, Tokenize, Leverage
(B) Estimate, Test, Label
(C) Extract, Transform, Load
(D) Explore, Train, Learn