Which data pre-processing technique is commonly used to handle missing data in a dataset?a.Feature scalingb.Outlier detectionc.Imputationd.Principal Component Analysis (PCA)

Which of the following is most likely best practice when preparing your data for a machine learning algorithm?Group of answer choicesImputing any missing data with randomly generated valuesEnsuring that all features/variables are on different scalesExtracting the most relevant features by performing a Principal Component AnalysisRemoving all outliers

In which of the following step the missing values are addressed ?  A. Data Cleaning  B. Data Collection  C. Data Arrangement  D. Data Gathering

When in the data preprocessing phase, how should one treat missing/null values?Remove the rows with missing values.Fill Missing values with the most common value in the column.Remove the whole column if it has more than 2% of the total size of the dataset as missing values.

Which of the following techniques would perform better for reducing dimensions of a data set?(1 Point)removing columns which have too many missing valuesremoving columns which have high variance in dataremoving columns with dissimilar data trendsnone of these

.Dimension reduction falls under which data pre-processing type?  A. Data Cleaning  B. Data Integration  C. Data Reduction  D. Data Transformation