Join Barton Poulson for an in-depth discussion in this video Classification in Python, part of Data Science Foundations: Data Mining.
- [Instructor] As with our other examples,…the first thing we're going to do…is we're going to install some packages.…You may already have them installed,…but it's good to make sure.…And it doesn't hurt to run them again.…Here we have, they're getting installed.…And next we actually need to import them…to make them available to the program this time around.…Again, that warning in red simply tells us…that matplotlib is building its font cache.…It's done by now, so it's nothing to worry about.…
We're gonna load the data, a .csv.…And we're going to use pandas read underscore csv.…Again, slightly different way of specifying the file path…if you're using a Mac or Windows.…I'm on a Mac, and I've got the file saved on a desktop,…so I can use this first one and save it into an object…called cc for credit card default.…If you're using Windows, you'll want to use this format,…except replace the word bart there in the middle…with the name of your own directory.…Either one will then create the object ccdefault…and then we can look at the columns or the variables…
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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