As mentioned before, the main objective of a data visualization is to effectively represent and communicate insights gained from a given set of data. This involves, as we have seen, removing pointless features from our plot, but also selecting the appropriate type of chart. Every chart type has its own narrative, and getting to know these narratives is necessary if you want to increase the level of effectiveness of your data visualization.
- 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
Not every chart is good for your message
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