What is the frequent category imputation in machine learning?

If a column contains only numerical data, then we can use mean or median imputation to fill in the missing values of the column. But if a column contains categorical values, then we mostly use most frequent category imputation for filling in the missing values of the column.

Please note that the most frequent value in a column is also the mode of the values of the column. Hence, to fill in the missing categorical values, we can calculate the mode of the data and then, use the mode to fill in the missing values.

How to perform the frequent category imputation in machine learning?

Let’s read the titanic dataset. If we print the percentage of missing values in each column of the dataset, we will see some values are missing from the embark town column.

import seaborn

df = seaborn.load_dataset("titanic")
print(df.isnull().mean()*100)

The output shows the following:

survived        0.000000
pclass          0.000000
sex             0.000000
age            19.865320
sibsp           0.000000
parch           0.000000
fare            0.000000
embarked        0.224467
class           0.000000
who             0.000000
adult_male      0.000000
deck           77.216611
embark_town     0.224467
alive           0.000000
alone           0.000000
dtype: float64

So, there are almost 0.224467% missing values in the embark town column. So, let’s find out the most frequent value of the …