Join Barton Poulson for an in-depth discussion in this video Association analysis in Python, part of Data Science Foundations: Data Mining.
- [Instructor] Association analysis…is really a different kind of analysis,…and one of the things is we're gonna use different packages,…because it is a very specialized set of requirements.…We're going to use something called…the apriori package for this demonstration.…Now, it's not something that's included by default…with the Anaconda distribution that you may have,…so we need to install it separately.…Now, I'm gonna mention that I did…a substantial amount of testing.…I was able to make this work well on Macs.…I had trouble making it work on PCs,…so this particular demonstration will be exclusive…to Macintosh computers.…
The first thing we're going to do…is we're going to install the apriori package,…and everybody has gonna have to do that.…And then you need to import it, so it's available,…and there we go.…Next, we're going to do the data and the analysis…really kind of in one big group here.…We're going to use the same groceries.csv file…with the market basket data.…And we need to set a confidence level.…I'm gonna use 05.…
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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