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Python package for Outlier Removal Algorithm using z_score or iqr.

Project description

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

Run In IDLE

Run on Jupyter
Open terminal (cmd)
jupyter notebook
Create a new python3 file.

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.

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.

Project details


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