Book Content
chapters • 14h44m total length
1. Why to Choose R for Your Data Mining and Where to Start
2. A First Primer on Data Mining Analysing Your Bank Account Data
3. The Data Mining Process - CRISP-DM Methodology
4. Keeping the House Clean – The Data Mining Architecture
5. How to Address a Data Mining Problem – Data Cleaning and Validation
6. Looking into Your Data Eyes – Exploratory Data Analysis
7. Our First Guess – a Linear Regression
8. A Gentle Introduction to Model Performance Evaluation
9. Don't Give up – Power up Your Regression Including Multiple Variables
10. Addressing the Problem from Another Perspective Through Classification Models
11. The Final Clash – Random Forests and Ensemble Learning
12. Looking for the Culprit – Text Data Mining with R
13. Sharing Your Stories with Your Stakeholders through R Markdown
14. Epilogue
15. Appendix A: Dealing with Dates, Relative Paths and Functions
