Metadata-Version: 2.1
Name: missing-values-yash-saxena
Version: 1.0.2
Summary: Replacing missing values in the dataset with the mean of that particular column using SimpleImputer class.
Home-page: UNKNOWN
Author: Yash Saxena
Author-email: yash972saxena@gmail.com
License: MIT
Description: # Replacing missing values in a dataset with the mean of that particular column
        
        **Project 3 : UCS633 DATA ANALYTICS AND VISUALIZATION**
        
        
        Submitted By: **Yash Saxena 101703627**
        
        ***
        pypi: <https://pypi.org/project/missing-values-yash-saxena/>
        ***
        
        ## SimpleImputer Class
        ```
        class sklearn.impute.SimpleImputer(missing_values=nan, strategy='mean', fill_value=None, verbose=0, copy=True, add_indicator=False)
        ```
        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>
        <br>
        missing_data : The missing_data placeholder which has to be imputed. By default is NaN.
        <br>
        <br>
        stategy : The data which will replace the NaN values from the dataset. The strategy argument can take the values â€“ 'mean'(default),'median', 'most_frequent' and 'constant'.
        <br>
        <br>
        fill_value : The constant value to be given to the NaN data using the constant strategy.
        <br>
        <br>
        copy : boolean, default=True
        If True, a copy of X will be created. If False, imputation will be done in-place whenever possible. Note that, in the following cases, a new copy will always be made, even if copy=False
        <br>
        <br>
        add_indicator : boolean, default=False
        If True, a MissingIndicator transform will stack onto output of the imputerâ€™s transform. This allows a predictive estimator to account for missingness despite imputation. 
        
        
        ## Installation
        
        Use the package manager [pip](https://pip.pypa.io/en/stable/) to install removal system.
        
        ```bash
        pip install missing-values-yash-saxena
        ```
        
        <br>
        
        ## How to use this package:
        
        missing-values-yash-saxena can be run as done below:
        
        
        
        ### In Command Prompt
        ```
        >> missing_values dataset.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: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
