Join Barton Poulson for an in-depth discussion in this video Regression analysis in R, part of Data Science Foundations: Data Mining.
- [Instructor] What this data set is, is chemical…evaluations of a large quantity of red wines,…thing like fixed acidity, residual sugar,…density, pH, and so on.…And then, it finishes with a…subjective judgement of the quality.…That's our last column in L.…And what we're going to try to do is take these…chemical measurements, and see how well they can predict…the subjective quality of the red wine.…The first thing we need to do is come down and…install or load a few packages.…I'm using the pacman package to get those…either installed or loaded.…
And then, we'll import the data set.…I've saved it to my desktop and I'm going to…save it into an object data.…And you can see I've got that loaded…in my environment in the top right.…Now, to make life a little easier, we're going to…define two variable groups.…One that has the predictor variable,…so that's everything except the last column,…we'll just call that x.…And then we'll save the outcome variable,…the judgement of quality, to y.…And then we're gonna use a few…different methods of regression.…
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
Big Data Foundations: Program Managementwith 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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