Join Barton Poulson for an in-depth discussion in this video Regression analysis in RapidMiner, part of Data Science Foundations: Data Mining.
- [Speaker] The first step is to import the process.…We'll go to file, import process,…it's on my desktop so I'll go there.…And there's the process.…I'll open that.…It shows up.…Now I'm going to save this so we have it…in my repository.…File, save process as,…that's the same name it had on my desktop.…And you can see that it's showing up right there.…
We also need to add the data.…So on my computer, on the desktop,…and there it is the CSV file.…Now it's correctly read that we have a header row…and we're scrolling through here,…but we are gonna need to do one thing.…Most of these variables are chemical measurements…of quality of red wine,…and those are fine.…But we're doing regression and we need…to identify an outcome variable.…And so, what we're going to do here is come to quality,…which is the perceived quality…and we're going to click on that one…and change it's role.…
Specifically we're going to call it label.…That's the one you use for the outcome.…Click Okay.…And now you can see that it's marked differently.…We'll hit next.…
Barton Poulson covers data sources and types, the languages and software used in data mining (including R and Python), and specific task-based lessons that help you practice the most common data-mining techniques: text mining, data clustering, association analysis, and more. This course is an absolute necessity for those interested in joining the data science workforce, and for those who need to obtain more experience in data mining.
- Prerequisites for data mining
- Data mining using R, Python, Orange, and RapidMiner
- Data reduction
- Data clustering
- Anomaly detection
- Association analysis
- Regression analysis
- Sequence mining
- Text mining
Skill Level Beginner
Transitioning from Data Warehousing to Big Datawith Alan Simon1h 50m Intermediate
Manage Your Organization's Big Data Programwith Alan Simon1h 11m Intermediate
2. Data Reduction
5. Anomaly Detection
6. Association Analysis
7. Regression Analysis
8. Sequential Patterns
9. Text Mining
Next steps1m 18s
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