Skip to content

1-Outlier detection and removal of the outlier by Using IQR The Data points consider outliers if it's below the first quartile or above the third quartile 2-Remove the Outliers by using the percentile 3-Remove the outliers by using zscore and standard deviation

Notifications You must be signed in to change notification settings

DrSara9888/Machaine-Learning-Big-Data

Repository files navigation

Machaine-Learning-Big-Data Data Quilaty

Cleaning Data and removing outlires are essential in any Big Data project. The results of the any project are more efficient if you prepare your Data. Decision making must be built on Data with high quilaty. In this folder I am using three ways to remove the outliers: 1-Outlier detection and removal of the outlier by Using IQR The Data points consider outliers if it's below the first quartile or above the third quartile 2-Remove the Outliers by using the percentile 3-Remove the outliers by using zscore and standard deviation

About

1-Outlier detection and removal of the outlier by Using IQR The Data points consider outliers if it's below the first quartile or above the third quartile 2-Remove the Outliers by using the percentile 3-Remove the outliers by using zscore and standard deviation

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages