Metadata-Version: 2.1
Name: missingvalues-101703297-ucs633
Version: 1.0.1
Summary: A python package to handle Missing Values using SimpleImputer Class
Home-page: UNKNOWN
Author: Kunal Bajaj
Author-email: kbajaj_be17@thapar.edu
License: UNKNOWN
Description: # Handling Missing Values using SimpleImputer Class
        
        **Project 3 : UCS633**
        
        
        Submitted By: **Kunal Bajaj 101703297**
        
        ***
        pypi: <https://pypi.org/project/missingvalues-101703297-thapar/>
        ***
        
        ## 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-101703297-ucs633
        
        ```bash
        pip install missingvalues-101703297
        ```
        <br>
        
        ## How to use this package:
        
        missingvalues-101703297 can be run as shown below:
        
        
        ### In Command Prompt
        ```
        >> missingvalues-101703297 data.csv
        ```
        <br>
        
        
        ## 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
        
        <br>
        
        
        ## 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
        
        <br>
        
        It is clearly visible that the rows,columns containing Null Values have been Handled Successfully.
        
        
        ## 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
