Handling Missing Values using SimpleImputer Class
Project 3 : UCS633
Submitted By: Kshitiz Varshney 101703295
pypi: https://pypi.org/project/missingValues-kvarshney-101703295/
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-kvarshney-101703295.
pip install missingValues-kvarshney-101703295How to use this package:
missingValues-kvarshney-101703295 can be run as shown below:
In Command Prompt
>> missingValues dataset.csv
Input dataset
| a | b | c |
|---|---|---|
| 0 | NaN | 4 |
| 2 | NaN | 4 |
| 1 | 7 | 0 |
| 1 | 3 | 9 |
| 7 | 4 | 9 |
| 2 | 6 | 9 |
| 9 | 6 | 4 |
| 3 | 0 | 9 |
| 9 | 0 | 1 |
Output Dataset after Handling the Missing Values
| a | b | c |
|---|---|---|
| 0 | 4 | 4 |
| 2 | 4 | 4 |
| 1 | 7 | 0 |
| 1 | 3 | 9 |
| 7 | 4 | 9 |
| 2 | 6 | 9 |
| 9 | 6 | 4 |
| 3 | 0 | 9 |
| 9 | 0 | 1 |
It is clearly visible that the rows,columns containing Null Values have been Handled Successfully using median values.
