Join Barton Poulson for an in-depth discussion in this video Classification in R, part of Data Science Foundations: Data Mining.
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- [Narrator] The data set that I'm using…comes from the University of California at Irvine…Machine Learning Repository…and that's the UCI anteater right there.…And this one is called default of credit card clients…Data Set and it has 30,000 cases,…that's the number of instances,…and it has 24 different attributes,…and this tells us who donated it,…the original purposes, and describes what the variables are.…Now, I've taken most of that information…and I've repeated it in a text file…that's included with the assets,…Default of Credit Card Clients Data Set…where I gave it the url that it came from,…copy that information, and then at the bottom,…I did a few things.…
First off, when you download the data set…it comes as an Excel file and it has two rows of headers…one with sort of cryptic names for the variables X1 to X25,…and a second row at the top with more descriptive names.…I got rid of the X1 to X25, left the full variable names,…I converted the XLS Excel file to a CSV file…and I renamed it ccdefault for credit card default .csv.…
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
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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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