The correct answer is FALSE.
Dimensionality reduction algorithms are used to reduce the number of features in a dataset without losing too much information. This can be useful for a variety of tasks, such as data visualization, machine learning, and data compression.
In order to apply dimensionality reduction algorithms, it is necessary to have a target variable. The target variable is the variable that we are trying to predict or understand. For example, if we are trying to predict customer churn, the target variable would be whether or not a customer will churn.
Without a target variable, it is not possible to apply dimensionality reduction algorithms. This is because dimensionality reduction algorithms need to know which features are important for predicting the target variable. Without a target variable, it is not possible to know which features are important.
Therefore, the answer to the question “It is not necessary to have a target variable for applying dimensionality reduction algorithms” is FALSE.