A great way to obtain clarity is to look for simplicity. We already had a look at this while talking about data visualization in the second chapter. Simplicity implies reasoning on the main purpose of something, and removing everything that is unnecessary for this purpose, as it means removing all unnecessary things. Simplicity is the secret of nature, where every part is necessary and not redundant, and even beauty has its own purpose.
- 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
Clarity and simplicity
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