100 Days of Machine Learning | CampusX - Handling missing data | Numerical Data | Simple Imputer

Simple Imputer is a practical solution for filling missing numerical values in a dataset. This method replaces missing entries with the mean, median, or a specified constant, providing a straightforward approach to address and mitigate the impact of missing numerical data in your dataset. Code Used: https://github.com/campusx-official/100-days-of-machine-learning/upload/main/day36-imputing-numerical-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] ⌚Time Stamps⌚ 00:00 - Intro 00:37 - Handling Missing Numerical Data 03:33 - Mean / Median Imputation 07:55 - Code Demo 17:15 - Imputation using SKlearn 20:15 - Arbitarry Value Imputation 22:40 - Code Demo 25:57 - End of Distribution Imputation 30:09 - Outro