outlier-asharma-3027 1.1

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

Outlier Removal Using InterQuartile Range

Project 2 : UCS633

Submitted By: Abhishek Sharma - 101703027

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)

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