100 Days of Machine Learning | CampusX - Handling Missing Categorical Data | Simple Imputer | Most Frequent Imputation | Missing Category Imp

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 ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in/s/store ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at [email protected]