outlier-python-souravdlboy 1.0

Python package for Outlier Removal Algorithm using z_score or iqr.

outlier-python

Package Description :

Python package for Outlier Removal Algorithm using z_score or iqr.

Motivation :

This is a part of project - II made for UCS633 - Data analytics and visualization at TIET.
@Author : Sourav Kumar
@Roll no. : 101883068

Algorithm :

• Z-SCORE : If the population mean and population standard deviation are known, the standard score of a raw score x is calculated as:
  z = (x - mean) / std.
  mean : is the mean of the sample.
  std : is the standard deviation of the sample.

• Interquartile range : interquartile range (IQR), also called the midspread, middle 50%, or H‑spread, is a measure of statistical dispersion, being equal to the difference between 75th and 25th percentiles, or between upper and lower quartiles.
  IQR = Q3 − Q1
  The IQR of a set of values is calculated as the difference between the upper and lower quartiles, Q3 and Q1. Each quartile is a median calculated as follows :
  Given an even 2n or odd 2n+1 number of values.
  first quartile Q1 = median of the n smallest values
  third quartile Q3 = median of the n largest values
  The second quartile Q2 is the same as the ordinary median.

Getting started Locally :

Run On Terminal
python -m outlier.outlier inputFilePath outputFilePath z_score
or python -m outlier.outlier inputFilePath outputFilePath iqr
ex. python -m outlier outlier C:/Users/DELL/Desktop/train.csv C:/Users/DELL/Desktop/output.csv z_score

Run In IDLE
from outlier import outlier
o = outlier.outlier(inputFilePath, outputFilePath)
o.outlier_main('z_score') or
o.outlier_main('iqr')

Run on Jupyter
Open terminal (cmd)
jupyter notebook
Create a new python3 file.
from outlier import outlier
o = outlier.outlier(inputFilePath, outputFilePath) o.outlier_main('z_score') or
o.outlier_main('iqr')

• NOTE : outlier_main() doesn't necessarily require any method argument , if no argument is provided, it uses z_score by default as the algorithm for removal of outliers from the dataset.
• The algorithm only reports missing data containing columns and not handles them, it assumes that it has been handled already.
  Also in case of z-score method, it will not affect much, but it may be possible to give wrong output in case of IQR if missing values are found.

OUTPUT :

Removes all the valid rows contaning outlier values from the dataset and prints the number of rows removed along with the columns which were considered for the algorithm.
Also , the final dataframe will be written to the output file path you provided.

TESTING :

• The package has been extensively tested on various datasets consisting varied types of expected and unexpected input data and any preprocessing , if required has been taken care of.