Your First Machine Learning Project - Crash Course - How to impute missing data in categorical features (using MICE)

Welcome to the tenth video of the series "Build your First Machine Learning Project". In this, we'll see how to impute categorical data using MICE in Python. This video will provide in-depth information on imputing categorical data with python codes walk through. So let's understand it. Chapters 0:00 - 0:33 Intro 0:34- 4:52 How to impute categorical data using MICE 4:53 -8:24 Begin MICE imputation 8:24-8:40 Conclusion In order to make the best out of this, please watch this series in the order in playlist: Build Your First ML Model Playlist: https://www.youtube.com/watch?v=KSsjPbowHQ0&list=PLFAYD0dt5xCymcvacfR4CLB9Pk_9L50gz Previous Lesson: Impute Missing Values using MICE : https://youtu.be/BjyUbk258o4 Earlier Lessons: 1. Build your first ML Project: https://youtu.be/KSsjPbowHQ0 2. How to Formulate ML Problem: https://youtu.be/ygayqatDEDk 3. Setup Python Environment: https://youtu.be/Yk9BFMO6QXE 4. Jupyter Notebook Tutorial: https://youtu.be/4yuo96HtTw8 5. What is ML Modeling: https://youtu.be/Bcfk4HKgC5E 6. Reduce the size of Pandas Dataframe: https://youtu.be/Xa26NB75htg 7. What is EDA: https://youtu.be/rqCZZBrfNak 8. How to impute missing Data: https://youtu.be/Qir0Qi_CD2o Let me know in the comments section if you have any questions! If you enjoyed this video, be sure to throw it a like and make sure to subscribe to not miss any future videos! Thanks for watching! #mlmodeling, #python, #machinelearning, #artificialintelligence, #pandas, #datascience