Categorical Data Imputation in Python using Predictive Models
In the previous video, we explored the most common approach of filling in missing values with the mode (the most frequent level) 🛠️ and also dived into similarity profiling to handle those blanks! 🧠✨
Previous video: • A Better Approach to Categorical Data...
Complete Data Preparation Playlist - https://tinyurl.com/yc4fmdpm
Now, in this video, we’re taking it up a notch! 🔥 Instead of replacing missing values with a single label, we’re using a predictive model to assign different labels to different observations 🎯📊. Here’s the magic 🧙♂️: We treat the variable with missing values as the target column, and all other features act as predictors. Unlike before, where the imputation was uniform, this method generates different values for each missing entry! 🌟🔄
⚠️ Important Tip: When creating your train and test sets, make sure your train data has no missing values for the feature being imputed, while the test data contains all the missing values that need treatment. This ensures accurate predictions! 💡✅
Join us as we walk through a complete hands-on Python tutorial 👩💻🐍, making sure you’re all set to tackle missing values with confidence! 💪🚀 Let’s make your data even more powerful! 📈✨
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