Скачать или смотреть A Better Approach to Categorical Data Imputation in Python

Handling missing values in data preparation is a crucial step, especially when dealing with categorical features. 🧐 While we have various methods to tackle missing values in numerical features—like using mean, median, or even advanced techniques like KNN or iterative imputation—the options for categorical features are often limited. Most data practitioners tend to use the mode to fill in these gaps, but this univariate approach might not always be the best choice. 😕 What if there was a way to consider the relationship between all features to make a more informed decision? That's what we explore in this video! 🎯

Complete Data Preparation Playlist - https://tinyurl.com/yc4fmdpm

✨ Step 1: Label as ‘Unknown’
First, we label the missing values in our categorical feature as ‘Unknown.’ This allows us to distinguish these cases without immediately jumping to impute them with the most frequent category (mode). This step sets the stage for a more nuanced analysis. 👀

✨ Step 2: Convert to Numeric
Next, we convert the rest of the dataset to a numeric form, which allows us to perform mathematical operations and comparisons across different features. This step is essential for the profiling we’ll do later on. 🔢

✨ Step 3: Similarity Profiling
Here’s where it gets interesting. We perform a similarity profiling by grouping the dataset by the categorical feature—including the 'Unknown' label—and aggregating the mean values of all the other features. 🧠 This lets us observe how the 'Unknown' group compares to other categories in the feature. By considering all the features rather than just one, we get a multivariate view of how the 'Unknown' category fits into the data landscape. 🌍

Why This Approach is Superior
Unlike the simple mode imputation, which only looks at the distribution of a single feature, this method uses all available information to make a more informed decision. It’s a multivariate approach, making it far more robust and accurate for data imputation. 🚀

By the end of this video, you'd have added a powerful new approach to your data preparation toolkit that goes beyond the basics and helps you make better, more informed decisions. 🧩 So, don’t miss out—let’s elevate your data science game together! 💪