Join Barton Poulson for an in-depth discussion in this video Regression analysis in KNIME, part of Data Science Foundations: Data Mining.
- [Instructor] The first thing we need to do…is read the data.…We use CSV readers.…I'm gonna option click on that and open up Configure.…What I need to do is simply tell it where the data is.…I have it on my desktop.…There it is.…That's quick and easy.…We just use the default settings for that.…Then we're going to do two kinds of regression.…First I'm going to do just a standard linear regression…with Linear Regression Learner.…We can option click on that or do F6…to open up its configuration options.…It knows to put quality up here because quality is…the last thing in the data set.…
Then I selected all of the other variables and put them here…and specified that they should be used in the predictions.…You can hit OK.…I've already got it so I'm gonna hit cancel.…Then you need to run it by Execute.…I've done that already, so now we can just open up…the Regression Result View.…And what we have here is a very simple table…and it's giving us the regression coefficients…for each of the predictor variables…along with their standard error, a t-value,…
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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