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: • Build Your FIRST Machine Learning Project ...
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Previous Lesson:
Impute Missing Values using MICE : • Multiple Imputation by Chained Equations (...
Earlier Lessons:
1. Build your first ML Project: • Build Your FIRST Machine Learning Project ...
2. How to Formulate ML Problem: • Build Your First ML Project part 2: How t...
3. Setup Python Environment: • Setup Python Environment using ANACONDA
4. Jupyter Notebook Tutorial: • Jupyter Notebook Tutorial - How to Install...
5. What is ML Modeling: • What is ML Modeling? (Problem statement an...
6. Reduce the size of Pandas Dataframe: • Reduce the memory size of Pandas Dataframe...
7. What is EDA: • Exploratory Data Analysis (EDA) - Use thes...
8. How to impute missing Data: • How to handle missing data for machine lea...
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