Explore the critical role data construction plays in data preparation for predictive analytics.
- [Instructor] Pretty much everything…we've been talking about in regards to data preparation…is a kind of feature engineering.…Now, the cross-industry standard process…for data mining likes to call this data construction…but the more common term these days…is feature engineering.…What it basically is is extracting the meaning…out of variables by using a whole bunch…of different formulas,…so we've already seen that we might calculate a ratio…or a delta comparing recent behavior…to baseline behavior.…
We generate all kinds of variables…through aggregating and restructuring.…But once it's already in its basic form,…one row per customer, or one row per patient,…one row per loan, we're still going to continue doing formulas.…Let me show you a couple of quick examples.…Dates, for instance, are not very useful…to these predictive models…but the gaps between dates is very interesting to modeling,…so we're going to be doing what's called date arithmetic.…You also might extract a fact…about a date that it was a day during the business week…
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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