Outlier Removal Using InterQuartile Range

Project 2 : UCS633

    Submitted By: **Kshitiz Varshney 101703295**
    
    ***
    pypi: <https://pypi.org/project/outlierRemoval-kvarshney-101703295/>
    ***
    
    ## 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 value of the dataset.
    <br>
    The first quartile Q1, which represents a quarter(25%) of the way through the list of all data.
    <br>
    The median of the data set, which represents the midpoint(50%) of the whole list of data.
    <br>
    The third quartile Q3, which represents three-quarters(75%) of the way through the list of all data.
    <br>
    The maximum value of the data set.
    <br>
    <br>
    These five values helps in determining exceptional(outliers) elements present in our dataset.
    
    ## 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 outlierRemoval-kvarshney-101703295.
    
    ```bash
    pip install outlierRemoval-kvarshney-101703295
    ```
    <br>
    
    ## How to use this package:
    
    outlierRemoval-kvarshney-101703295 can be run as shown below:
    
    
    ### In Command Prompt
    ```
    >> outlierRemoval dataset.csv
    ```
    <br>
    
    
    ## Sample dataset
    
    Marks | Students 
    :------------: | :-------------:
    3  | Student 1
    57 | Student 2
    65 | Student 3
    98 | Student 4
    43 | Student 5
    44 | Student 6
    54 | Student 7
    1  | Student 8
    
    <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 and Student8 have been removed due to them being Outliers.
    
    
    ## License
    [MIT](https://choosealicense.com/licenses/mit/)