100 Days of Machine Learning | CampusX - Missing Indicator | Random Sample Imputation | Handling Missing Data Part 4

The Missing Indicator method involves creating a binary indicator for missing values in a dataset, providing additional information on missing patterns. Random Sample Imputation, on the other hand, fills missing values with random samples from the observed data. These techniques offer alternative strategies for handling missing data in a dataset. Code Used: https://github.com/campusx-official/100-days-of-machine-learning/tree/main/day38-missing-indicator ============================ 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 00:12 - Revision 02:12 - What is Random Imputation? 08:35 - Code Demo using Titanic Dataset 21:33 - Missing Indicator 30:17 - Automatically selecting value for Imputation 36:36 - Outro