Metadata-Version: 2.1
Name: 101703311_OUTLIERS
Version: 1.0.2
Summary: A python package for removing outliers from a dataset using InterQuartile Range (IQR)
Home-page: UNKNOWN
Author: Lokesh Arora
Author-email: 3lokesharora@gmail.com
License: UNKNOWN
Description: # Outlier Removal Using InterQuartile Range
        
        **Project 2 : UCS633**
        
        
        Submitted By: **Lokesh Arora 101703311**
        
        ***
        pypi: <https://pypi.org/project/101703311_OUTLIERS/>
        ***
        
        ## InterQuartile Range (IQR) Description
        
        Any set of data can be described by its five-number summary. These five numbers, which give you the information you need to find patterns and outliers, consist of:
        
        The minimum or lowest value of the dataset.
        <br>
        The first quartile Q1, which represents a quarter of the way through the list of all data.
        <br>
        The median of the data set, which represents the midpoint of the whole list of data.
        <br>
        The third quartile Q3, which represents three-quarters of the way through the list of all data.
        <br>
        The maximum or highest value of the data set.
        <br>
        <br>
        These five numbers tell a person more about their data than looking at the numbers all at once could, or at least make this much easier.
        
        ## Calculation of IQR
        
        IQR = Q3 – Q1
        <br>
        MIN = Q1 - (1.5*IQR)
        <br>
        MAX = Q3 + (1.5*IQR)
        <br>
        
        ## Installation
        
        Use the package manager [pip](https://pip.pypa.io/en/stable/) to install 101703311_OUTLIERS.
        
        ```bash
        pip install 101703311_OUTLIERS
        ```
        <br>
        
        ## How to use this package:
        
        101703311_OUTLIERS can be run as shown below:
        
        
        ### In Command Prompt
        ```
        >> outlierRemoval dataset.csv
        ```
        <br>
        
        
        ## Sample dataset
        
        Marks | Students 
        :------------: | :-------------:
        3 | Student1
        57 | Student2
        65 | Student3
        98 | Student4
        43 | Student5
        44 | Student6
        54 | Student7
        99 | Student8
        1 | Student9
        
        <br>
        
        
        ## Output Dataset after Removal
        
        Marks | Students 
        :------------: | :-------------:
        57 | Student2
        65 | Student3
        98 | Student4
        43 | Student5
        44 | Student6
        54 | Student7
        
        <br>
        
        It is clearly visible that the rows containing Student1, Student8 and Student9 have been removed due to them being Outliers.
        
        
        ## 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
