Mozilla-Campus-Club-Cummins/Documentation_HacktoberFest2025

Data Cleaning

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#13 opened on Sep 28, 2024

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Description

Document techniques for handling missing data and outliers. Techniques and examples for data cleaning using Pandas and Scikit-learn.

Contributor guide

Research direction
Research common techniques for handling missing values (e.g., deletion, imputation) and outliers (e.g., IQR, Z score) using Pandas and Scikit learn, and create documented examples with code snippets.
Tech stack
pythonpandasscikit learn
Domain
datamachine learning
Issue type
Documentation
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
2
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
GitPythonPandasScikit learn
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
80