Fill Missing Values in Categorical Columns using Mode & 'Missing' word | Part 5 | Data Preprocessing

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In this video, we’ll explore how to handle missing values in categorical columns using both Pandas and Scikit-learn’s SimpleImputer class.

Handling missing data in categorical features is just as important as in numeric ones — and it's a key skill every data scientist must develop.

Today, you’ll learn how to fill missing values in categorical columns using two powerful techniques:

Mode Imputation (most frequent value)

Custom or Placeholder Value (like “Missing” or “Unknown”)

What You’ll Learn in This Video:

Why we use mode or custom placeholder for imputation

How to fill missing categories with a custom word like "Missing"

How to use Scikit-learn’s SimpleImputer for both strategies

⏳ Timestamps:
00:00 - Introduction: Categorical Imputation Strategies
00:40 - Theory about Topic
02:56 - Method 1: Fill values using Pandas
08:55 - Method 2: Using SimpleImputer for imputing values
12:58 - Windup

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👉 ML Journey: Day by Day

💻 Source Code on GitHub:
https://github.com/WasayRabbani/Machine-Learning-Tutorial

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Видео Fill Missing Values in Categorical Columns using Mode & 'Missing' word | Part 5 | Data Preprocessing канала MindWired