missingval-asharma-3027 1.0

A python package to handle Missing Values using SimpleImputer Class

Handling Missing Values using SimpleImputer Class

Project 3 : UCS633

Submitted By: **Abhishek Sharma - 101703027 **

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.

How to use this package:

missingval-asharma-3027 can be run as shown below:

In Command Prompt

>> missingValues dataset.csv

Sample dataset

a	b	c
NaN	7	0
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
3.777778	7	0
0	4.125	4
2	4.125	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.

License

[MIT]