Join Barton Poulson for an in-depth discussion in this video Regression analysis in Python, part of Data Science Foundations: Data Mining.
- [Instructor] As with our other examples of Python,…the first thing we need to do is install packages…so their code can be made available.…We'll do that with this command even though…if they're installed already,…it just lets you know they're already there.…We've got that.…And then we need to actually make the code available.…And you see that we're bringing in several functions…from scikit-learn,…a powerful and flexible machine learning package.…Next, we can feed in the data.…If you have a Macintosh, you use it this way.…You specify the file path like this.…
I'm using winequality-red.csv.…It's on the desktop.…If you have a Windows computer, use this file path…but remember to change bart to your own directory name.…Either one of them feeds it into an object called df,…or data frame, and now let's take a look…at the first few lines of that data frame.…What we have are a lot of chemical measurements:…fixed acidity, residual sugar, so on and so forth,…about various kinds of red wine,…and we have the perceived quality there on the far right.…
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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