outlierRemoval-kvarshney-101703295 1.0.2

A python package for removing outliers from a dataset using InterQuartile Range (IQR)

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 value of the data set.

These five values helps in determining exceptional(outliers) elements 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. bash 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 | Student 1 57 | Student 2 65 | Student 3 98 | Student 4 43 | Student 5 44 | Student 6 54 | Student 7 1 | Student 8
## 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 and Student8 have been removed due to them being Outliers. ## License MIT