Exercise

# KNN imputation of categorical values

Once all the categorical columns in the DataFrame have been converted to ordinal values, the DataFrame can be imputed. Imputing using statistical models like K-Nearest Neighbors provides better imputations.

In this exercise, you'll use the `KNN()`

function from `fancyimpute`

to impute the missing values. Lastly, you will also convert the ordinal values back to their respective categories using the ordinal encoder's `.inverse_transform()`

method. Remember, each column's encoder object is present in the `ordinal_enc_dict`

dictionary.
The `KNN()`

function and the ordinally encoded DataFrame `users`

has already been imported for you.

Instructions

**100 XP**

- Initialize the
`KNN()`

imputer. - Impute the
`users`

DataFrame and round the results. - Iterate over columns in
`users`

and perform`.inverse_tranform()`

on the ordinally encoded columns.