ML | Handling Missing Data with a Simple Imputer

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.

Exit

 Original Data: 
 [[12, nan, 34] [10, 32, nan] [nan, 11, 20]] 
 Imputed Data: 
 [[12, 21.5 , 34] [10, 32, 27] [11, 11, 20]] 

Remember: average her or median is taken along the matrix column