The correct answer is C. Data Reduction.
Data reduction is the process of reducing the dimensionality of data while retaining its most important features. This can be done through a variety of methods, such as principal component analysis (PCA), factor analysis, and t-distributed stochastic neighbor embedding (t-SNE).
Data imputation is the process of filling in missing values in a dataset. This can be done through a variety of methods, such as mean imputation, median imputation, and multiple imputation.
Data scaling is the process of transforming data so that it has a common scale. This can be done through a variety of methods, such as min-max scaling, z-score scaling, and rank-based scaling.
Data normalization is the process of transforming data so that it has a mean of 0 and a standard deviation of 1. This can be done through a variety of methods, such as mean-variance normalization, min-max normalization, and z-score normalization.
Here are some additional details about each of the options:
- Data Imputation is the process of filling in missing values in a dataset. This can be done through a variety of methods, such as mean imputation, median imputation, and multiple imputation. Mean imputation is the simplest method, and it involves replacing each missing value with the mean of the non-missing values. Median imputation is similar to mean imputation, but it replaces each missing value with the median of the non-missing values. Multiple imputation is a more sophisticated method, and it involves creating multiple datasets with the missing values replaced by different values. The results from the multiple datasets are then combined to produce a more accurate estimate of the missing values.
- Data Scaling is the process of transforming data so that it has a common scale. This can be done through a variety of methods, such as min-max scaling, z-score scaling, and rank-based scaling. Min-max scaling involves rescaling the data so that the minimum value is 0 and the maximum value is 1. Z-score scaling involves rescaling the data so that the mean is 0 and the standard deviation is 1. Rank-based scaling involves rescaling the data so that the ranks of the data points are preserved.
- Data Reduction is the process of reducing the dimensionality of data while retaining its most important features. This can be done through a variety of methods, such as principal component analysis (PCA), factor analysis, and t-distributed stochastic neighbor embedding (t-SNE). Principal component analysis (PCA) is a method that projects the data onto a lower-dimensional space while preserving as much of the variance in the data as possible. Factor analysis is a method that identifies a set of latent factors that explain the covariance structure of the data. T-distributed stochastic neighbor embedding (t-SNE) is a method that visualizes high-dimensional data in a low-dimensional space.