Book Content
chapters • 11h4m total length
1. Setting GNU R for predictive modeling
2. Basic data visualization with tools built-in in R
3. Data visualization with lattice
4. Unsupervized learning: clustering with k-means
5. Unsupervized learning: Hierarchical clustering
6. Unsupervized learning: Principal Component Analysis
7. Unsupervized learning: market basket analyses with Apriori (association rules)
8. Probability Distributions, Covariance, and Correlation
9. Regression
10. Classification with naïve Bayes and k-nn
11. Decision trees
12. Multilevel regression in R
13. Text Analytics with R
14. PMML
15. Appendix, Solution to exercises
16. References
