This book covers common aspects in predictive modeling:
- A. Data Preparation / Data Profiling
- B. Selecting best variables (dataviz)
- C. Assessing model performance
- D. Miscellaneous
And it is heavly based on the funModeling
package from the R language . Please install before starting :)
install.packages("funModeling")
- Model creation consumes around 10% of almost any predictive modeling project;
funModeling
will try to cover remaining 90%. - It's not only the function itself, but the explanation of how to interpret results. This brings a deeper understanding of what is being done, boosting the freedom to use that knowledge in other situations regardless of the language.
Hopefully this book barerly has an end, it will be updated periodically. Next planned chapter is a case study in predictive modeling. And you can contribute! below the github link.
First published at: livebook.datascienceheroes.com
This book is under Attribution-NonCommercial-ShareAlike 4.0 International license.