If you are a budding data scientist, or a data analyst with a basic knowledge of R, and want to get into the intricacies of data mining in a practical manner, this is the book for you. No previous experience of data mining is required.

• Master relevant packages such as dplyr, ggplot2 and so on for data mining
• Learn how to effectively organize a data mining project through the CRISP-DM methodology
• Implement data cleaning and validation tasks to get your data ready for data mining activities
• Execute Exploratory Data Analysis both the numerical and the graphical way
• Develop simple and multiple regression models along with logistic regression
• Apply basic ensemble learning techniques to join together results from different data mining models
• Perform text mining analysis from unstructured pdf files and textual data
• Produce reports to mass communicate objectives, methods, and insights of your analyses
  

Chapter 1: Why to Choose R for Your Data Mining and Where to Start
Chapter 2: A First Primer on Data Mining Analysing Your Bank Account Data
Chapter 3: The Data Mining Process - CRISP-DM Methodology
Chapter 4: Keeping the House Clean – The Data Mining Architecture
Chapter 5: How to Address a Data Mining Problem - Data Cleaning and Validation
Chapter 6: Looking into Your Data Eyes - Exploratory Data Analysis
Chapter 7: Our First Guess - a Linear Regression
Chapter 8: A Gentle Introduction to Model Performance Evaluation Chapter 9: Don't Give up – Power up Your Regression Including Multiple Variables Chapter 10: A Different Outlook to Problems with Classification Models


Chapter 11: The Final Clash - Random Forests and Ensemble Learning
Chapter 12: Looking for the Culprit – Text Data Mining with R
Chapter 13: Sharing Your Stories with Your Stakeholders through R Markdown
Chapter 14: Epilogue
Chapter 15: Dealing with Dates, Relative Paths And Functions