SimpleImputer — it is a training class that helps to handle missing data in the predictive model dataset. It replaces NaN values with the specified placeholder.
This is done with the SimpleImputer () method, which takes the following arguments:
missing_data : The missing_data placeholder which has to be imputed. By default is
stategy : The data which will replace the NaN values from the dataset. The strategy argument can take the values - `mean` (default),` median `,` most_frequent `and` constant `.
fill_value : The constant value to be given to the NaN data using the constant strategy.
Code: Python code illustrating the use of the SimpleImputer class.
< code class = "functions"> print
[[12, nan, 34] [10, 32, nan] [nan, 11, 20]]
[[12, 21.5 , 34] [10, 32, 27] [11, 11, 20]]
Remember: average her or median is taken along the matrix column