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A python package to handle Missing Values using SimpleImputer Class

Project description

Handling Missing Values using SimpleImputer Class

Project 3 : UCS633

Submitted By: Kunal Bajaj 101703297


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.


Use the package manager pip to install missingvalues-101703297-ucs633

pip install missingvalues-101703297

How to use this package:

missingvalues-101703297 can be run as shown below:

In Command Prompt

>> missingvalues-101703297 data.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.



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