What is the best solution to handle missing values in a categorical feature?

What is the best solution to handle missing values in a categorical feature?

The most widely used methods are Create a New Category (Random Category) for NAN Values and Most frequent category imputation.

Could be used to replace the missing value for a categorical variable?

Mode imputation
For categorical variables Mode imputation means replacing missing values by the mode, or the most frequent- category value.

How do you find the mode of categorical data in Python?

Python | Pandas dataframe. mode()

1. Syntax: DataFrame.mode(axis=0, numeric_only=False)
2. Parameters :
3. axis : get mode of each column1, get mode of each row.
4. numeric_only : if True, only apply to numeric columns.

How should you handle categorical features?

One-Hot Encoding is the most common, correct way to deal with non-ordinal categorical data. It consists of creating an additional feature for each group of the categorical feature and mark each observation belonging (Value=1) or not (Value=0) to that group.

How can I handle missing categorical data that has?

The easiest way to handle missing categorical data with out imputing is to just treat it as a category itself

How to handle missing values categorical features Vidhya?

Replacing null data with the most frequent ones or deleting the rows can be a convenient solution in case we have few missing data or a very large dataset. On the contrary, in case we have a lot of missing values or a small dataset, a prediction/clustering could save us valuable information that would otherwise be lost.

How to impute missing values of categorical variables?

We first impute missing values by the median of the data. Median is the middle value of a set of data. To determine the median value in a sequence of numbers, the numbers must first be arranged in ascending order. We can also do this by using SimpleImputer class.

How to deal with missing data in analytics?

Our first basic approach will be to create a model without the column maker. This “no-deal” practice is necessary when the large amount of missing data threatens to invalidate the entire feature. With this scenario we will probably have the worst scores on metrics, but we can use them to compare the other methods