Handling Missing Values using SimpleImputer Class

Project 3 : UCS633

Submitted By: Kunal Jindal 101703299

pypi: https://pypi.org/project/missingValues-kjindal-101703299/

SimpleImputer Class

SimpleImputer is a scikit-learn class which is helpful in handling the missing data in the predictive model dataset. It replaces the NaN values with a specified placeholder. It is implemented by the use of the SimpleImputer() method which takes the following arguments:
missing_data : The missing_data placeholder which has to be imputed. By default is NaN.
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.

Installation

Use the package manager pip to install missingValues-kjindal-101703299.

pip install missingValues-kjindal-101703299

How to use this package:

missingValues-kjindal-101703299 can be run as shown below:

In Command Prompt

>> missingValues dataset.csv

Sample dataset

Output Dataset after Handling the Missing Values

It is clearly visible that the rows,columns containing Null Values have been Handled Successfully.

License

MIT