outlierRemoval-kjindal-101703299 1.0.2

Description:

outlierRemovalkjindal101703299 1.0.2

Outlier Removal Using InterQuartile Range
Project 2 : UCS633
Submitted By: Kunal Jindal 101703299

pypi: https://pypi.org/project/outlierRemoval-kjindal-101703299/

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.

The first quartile Q1, which represents a quarter of the way through the list of all data.

The median of the data set, which represents the midpoint of the whole list of data.

The third quartile Q3, which represents three-quarters of the way through the list of all data.

The maximum or highest value of the data set.


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

MIN = Q1 - (1.5IQR)

MAX = Q3 + (1.5IQR)

Installation
Use the package manager pip to install outlierRemoval-kjindal-101703299.
pip install outlierRemoval-kjindal-101703299


How to use this package:
outlierRemoval-kjindal-101703299 can be run as shown below:
In Command Prompt
>> outlierRemoval dataset.csv


Sample dataset



Marks
Students




3
Student1


57
Student2


65
Student3


98
Student4


43
Student5


44
Student6


54
Student7


99
Student8


1
Student9




Output Dataset after Removal



Marks
Students




57
Student2


65
Student3


98
Student4


43
Student5


44
Student6


54
Student7




It is clearly visible that the rows containing Student1, Student8 and Student9 have been removed due to them being Outliers.
License
MIT