To increase model efficiency

To distort descriptive statistics

To identify data points that deviate significantly from the rest

To increase the variability in the dataset

Identifies unique categories in the categorical variable

Resets the index of the dataframe

Applies the encoding to the data

Creates binary vectors for each category

To eliminate categorical variables

To enable mathematical computations

To enhance model interpretability

To make data more complex

To enable mathematical computations

To reduce computational efficiency

To enhance model interpretability

To make data more complex

Outliers can lead to biased and less accurate models

Models become more accurate

Outliers reduce the noise in the data

Outliers do not affect machine learning models

The removal of duplicate rows is optional and has no impact on data quality.

The removal of duplicate rows is only performed when there is insufficient space in the database.

The removal of duplicate rows is not essential in data cleansing.

The removal of duplicate rows is essential to ensure data integrity and consistency.