A python package to handle Missing Values using SimpleImputer Class
Project description
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]
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