Description:
outlierRemovalkvarshney101703295 1.0.3
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.
The first quartile Q1, which represents a quarter(25%) of the way through the list of all data.
The median of the data set, which represents the midpoint(50%) of the whole list of data.
The third quartile Q3, which represents three-quarters(75%) of the way through the list of all data.
The maximum or highest value of the data set.
These five values helps us to determine outliers present in our dataset.
Calculation of IQR
IQR = Q3 – Q1
MIN = Q1 - (1.5IQR)
MAX = Q3 + (1.5IQR)
Installation
Use the package manager pip to install outlierRemoval-kvarshney-101703295.
pip install outlierRemoval-kvarshney-101703295
How to use this package:
outlierRemoval-kvarshney-101703295 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
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 andStudent8 have been removed due to them being Outliers.
License
MIT
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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