<reponame>ShAlireza/ML-Tries<gh_stars>0
"""

    Although the removal of m`issing data seems to be a convenient approach, it
    also comes with certain disadvantages; for example, we may end up removing
    too many samples, which will make a reliable analysis impossible. Or, if we
    remove too many feature columns, we will run the risk of losing valuable
    information that our classifier needs to discriminate between classes. In
    the next example, we will look at one of the most commonly used
    alternatives for dealing with missing values: interpolation techniques.`

"""

"""

    Although the removal of m`issing data seems to be a convenient approach, it
    also comes with certain disadvantages; for example, we may end up removing
    too many samples, which will make a reliable analysis impossible. Or, if we
    remove too many feature columns, we will run the risk of losing valuable
    information that our classifier needs to discriminate between classes. In
    the next example, we will look at one of the most commonly used
    alternatives for dealing with missing values: interpolation techniques.`

"""
Although the removal of m`issing data seems to be a convenient approach, it     also comes with certain disadvantages; for example, we may end up removing     too many samples, which will make a reliable analysis impossible. Or, if we     remove too many feature columns, we will run the risk of losing valuable     information that our classifier needs to discriminate between classes. In     the next example, we will look at one of the most commonly used     alternatives for dealing with missing values: interpolation techniques.`