Dirty Data

Project information

Dirty Data: Take a „broken" dataset (with nulls, duplicates, typos,...) and clean it collaboratively to produce a prestine dataset. (Dirty Dataset to practice Data Cleaning)

Work Flow Maintainer: Sets up repo, protects the main branch and manages the Issue Board Null Remove: Writes code to identify and impute missing values Outlier Detection: Uses techniques like boxplots to identify and flag statistical outliers Standardizer: Fixes string inconsistencies Visualizer: Creates,,before vs. after" histograms to prove the data structure hasn't been ruined Quality Assurance: Reviews every Merge Request to ensure the cleaning code doesn't delete valid data

dataset Python CSV