Handling Missing Categorical Data | Simple Imputer | Most Frequent Imputation | Missing Category Imp
Channel: CampusX
Date: 2021-04-24

For efficient data preprocessing, Simple Imputer and Most Frequent Imputation are effective techniques. Simple Imputer fills missing categorical values with a specified constant, while Most Frequent Imputation replaces them with the most frequent category in the column. These methods ensure a robust approach to addressing missing categorical data in your dataset.

Code Used: https://github.com/campusx-official/100-days-of-machine-learning/tree/main/day37-handling-missing-categorical-data

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