Metadata-Version: 2.1
Name: missingValues-kvarshney-101703295
Version: 1.0.3
Summary: A python package to handle Missing Values using SimpleImputer Class
Home-page: https://github.com/kv6737/missingValues
Author: Kshitiz Varshney
Author-email: kvarshney_be17@thapar.edu
License: UNKNOWN
Description: # 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:
        <br>
        missing_data : The missing_data placeholder which has to be imputed. By default is NaN.
        <br>
        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’.
        <br>
        fill_value : The constant value to be given to the NaN data using the constant strategy.
        
        
        ## Installation
        
        Use the package manager [pip](https://pip.pypa.io/en/stable/) to install missingValues-kvarshney-101703295.
        
        ```bash
        pip install missingValues-kvarshney-101703295
        ```
        <br>
        
        ## How to use this package:
        
        missingValues-kvarshney-101703295 can be run as shown below:
        
        
        ### In Command Prompt
        ```
        >> missingValues dataset.csv
        ```
        <br>
        
        
        ## 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 |
        
        
        <br>
        
        
        ## 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
        
        
        <br>
        
        It is clearly visible that the rows,columns containing Null Values have been Handled Successfully using median values.
        
        
        ## License
        [MIT](https://choosealicense.com/licenses/mit/)
        
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
