Learn about several different ways of handling missing data and the implications of the choice for scoring and deployment.
- [Instructor] Now we're going to talk about a topic…that could easily warrant its own course,…Modeling with Missing Data.…But we're going to try to stay laser focused…on the topic at hand, which is the implications…of the decisions that we make…throughout the entire process with a particular focus…on scoring and deployment.…You've got to be deployment focused.…100% of the times there's going to be some missing data.…Then what happens in most projects…is the modeler has to fix it and clean it…so that it's not missing.…
But then at scoring, it's missing again.…Everybody has to work together…to ensure that the model will work at scoring.…Modelers, especially if they're statisticians,…are trained to repair and address data issues.…This is a critical skill to have on the team,…but don't forget to ask yourself…if the data even belongs in the model.…For instance, before the mortgage crisis,…a lot of small real estate investors…could get so called no doc loans.…
Which got that name because they didn't have…to submit all of the usual paperwork.…
Note: This course is software agnostic. The emphasis is on strategy and planning. Examples, calculations, and software results shown are for training purposes only.
- Evaluating the proper amount of data
- Assessing data quality and quantity
- Seasonality and time alignment
- Data preparation challenges
- Data modeling challenges
- Scoring machine-learning models
- Deploying models and adjusting data prep and scoring
- Monitoring and maintenance
Skill Level Beginner
Machine Learning and AI Foundations: Recommendationswith Adam Geitgey58m 7s Intermediate
Deploying Scalable Machine Learning for Data Sciencewith Dan Sullivan1h 43m Intermediate
Defining terms1m 48s
1. The Phases of a Machine Learning Project
2. Designing a Machine Learning Dataset
3. Data Prep Challenges
4. Modeling Challenges
7. Monitoring and Maintenance
Next steps1m 1s
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