- Introduction to Statistical Learning, by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. The book treats in a more rigorous and formal way some of the most commonly employed statistical learning models and algorithms. A PDF version is also freely available at: http://www-bcf.usc.edu/~gareth/ISL/.
- Preface
- Why to Choose R for Your Data Mining and Where to Start
- A First Primer on Data Mining Analysing Your Bank Account Data
- The Data Mining Process - CRISP-DM Methodology
- Keeping the House Clean – The Data Mining Architecture
- How to Address a Data Mining Problem – Data Cleaning and Validation
- Looking into Your Data Eyes – Exploratory Data Analysis
- Our First Guess – a Linear Regression
- A Gentle Introduction to Model Performance Evaluation
- Don't Give up – Power up Your Regression Including Multiple Variables
- A Different Outlook to Problems with Classification Models
- What is classification and why do we need it?
- Logistic regression
- Support vector machines
- References
- Summary
- The Final Clash – Random Forests and Ensemble Learning
- Random forest
- Ensemble learning
- Applying estimated models on new data
- A more structured approach to predictive analytics
- Applying the majority vote ensemble technique on predicted data
- Further references
- Summary
- Looking for the Culprit – Text Data Mining with R
- Sharing Your Stories with Your Stakeholders through R Markdown
- Principles of a good data mining report
- Set up an rmarkdown report
- Develop an R markdown report in RStudio
- Rendering and sharing an R markdown report
- Further references
- Summary
- Epilogue
- Dealing with Dates, Relative Paths and Functions
Further references
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