Join Barton Poulson for an in-depth discussion in this video Classification in RapidMiner, part of Data Science Foundations: Data Mining.
- [Narrator] When we come to RapidMiner,…we have the same kind of busy interface…with a central empty canvas,…and what we're going to do is we're importing two things.…We're going to import the process,…and we're going to import the data set.…Now, in many other programs,…you can just double click on a file or hit Open…and bring it in to get the program.…RapidMiner likes to import,…and so we're going to come up here to File,…and Import Process.…Now, I've saved it to my desktop…and this is it right here with the .rmp…for RapidMiner process.…
I'll hit Open on that, and it looks like nothing's there,…but that's just because you have to scroll over sideways.…There we go.…Now we have lots of exclamation points here,…because it's not set up with the data yet,…and so it won't work.…We need to bring in data as well,…so we're going to go to Add Data,…to My Computer, to Desktop again,…and this is where you see the csv file,…the ccdefault, for credit card default,…_10k, for 10,000 rows.…I'm going to hit next,…and it's asking me about a header row…
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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