Dealing with Missing Data Like a Pro: An Adventure in Python

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Table of contents

• Understanding the Missing Data Landscape
• Detecting Missing Data
• Strategies for Handling Missing Data
• Quality Assurance — Verifying Imputations
• Domain-Specific Imputation
• Wrapping up

Welcome, fellow data enthusiasts and Python aficionados! Today, we embark on a thrilling journey to tackle one of the most common challenges in data science — missing data! Ah, those pesky gaps in our datasets that can ruin our models and give us headaches. But fear not! With Python as our trusty companion, we shall equip ourselves with the skills to handle missing data. So, fasten your seatbelts and let’s dive in!

The implementation is available below:

Portfolio/eda/missing-data.ipynb at main · smortezah/Portfolio

Understanding the Missing Data Landscape

Before we don our data superhero capes, let’s take a moment to understand the types of missing data we might encounter in our…