In this video, learn how to clean categorical features in whatever way necessary.
- [Instructor] Let's pick up right…where we left out in the last chapter…by reading in our dataset that has…all the continuous features cleaned.…And then we'll dive in to cleaning up…the categorical features in this lesson.…So you can go ahead and run that first cell,…and let's start by creating an indicator variable…for the cabin feature.…Now back in the exploring categorical features lesson,…we learned that unlike age, cabin is not missing at random.…We learned that two thirds of people aboard…that had cabins survived,…while only 30% of those aboard…that did not have cabins survived.…
So we're going to create this…really simple cabin indicator using the WHERE method…from NumPy that acts as an if statement.…So the condition we want to use is…whether cabin is null.…So I'll call titanic bracket cabin,…and then again will use this isnull method…which just returns a true or false based…on whether cabin is missing or not.…And so we'll say if cabin is missing,…we want it to return as zero.…
And if it's not missing, we'll return a one.…
- What is machine learning (ML)?
- ML vs. deep learning vs. AI
- Handling common challenges in ML
- Plotting continuous features
- Continuous and categorical data cleaning
- Measuring success
- Overfitting and underfitting
- Tuning hyperparameters
- Evaluating a model
Skill Level Beginner
Deploying Scalable Machine Learning for Data Sciencewith Dan Sullivan1h 43m Intermediate
1. Machine Learning Basics
2. Exploratory Data Analysis and Data Cleaning
3. Measuring Success
Why do we split up our data?5m 54s
4. Optimizing a Model
5. End-to-End Pipeline
Next steps1m 23s
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