Welcome to my Data Mining With Python and R tutorials! In these tutorials, you will learn a wide array of concepts about Python and R programing in Data Mining. The PDF version can be downloaded from HERE.

Contents

• 1. Preface
  • 1.1. About this tutorial
    • 1.1.1. About the authors
  • 1.2. Motivation for this tutorial
  • 1.3. Copyright notice and license info
  • 1.4. Acknowledgement
  • 1.5. Feedback and suggestions
• 2. Python or R for data analysis?
  • 2.1. Ponder over questions
  • 2.2. Comparison List
  • 2.3. My Opinions
• 3. Getting Started
  • 3.1. Installing programming language
  • 3.2. Installing programming platform
  • 3.3. Installing packages
• 4. Data Exploration
  • 4.1. Procedures
  • 4.2. Datasets in this Tutorial
  • 4.3. Loading Datasets
    • 4.3.1. Loading table format database
    • 4.3.2. Loading data from .csv
    • 4.3.3. Loading data from .xlsx
  • 4.4. Audit Data
    • 4.4.1. Check missing rate
    • 4.4.2. Checking zero variance features
  • 4.5. Understand Data With Statistics methods
    • 4.5.1. Summary of the data
    • 4.5.2. The size of the data
    • 4.5.3. Data type of the features
    • 4.5.4. The column names
    • 4.5.5. The first or last parts of the data
    • 4.5.6. Correlation Matrix
    • 4.5.7. Covariance Matrix
  • 4.6. Understand Data With Visualization
    • 4.6.1. Summary plot of data in figure
    • 4.6.2. Histogram of the quantitative predictors
    • 4.6.3. Boxplot of the quantitative predictors
    • 4.6.4. Correlation Matrix plot of the quantitative predictors
  • 4.7. Source Code for This Section
• 5. Data Manipulation
  • 5.1. Combining DataFrame
    • 5.1.1. Mutating Joins
    • 5.1.2. Filtering Joins
  • 5.2. DataFrame Operations
• 6. Pre-processing procedures
  • 6.1. Rough Pre-processing
    • 6.1.1. dealing with missing data
  • 6.2. Source Code for This Section
• 7. Summary of Data Mining Algorithms
  • 7.1. Diagram of Data Mining Algorithms
  • 7.2. Categories of Data Mining Algorithms
• 8. Dimension Reduction Algorithms
  • 8.1. What is dimension reduction?
  • 8.2. Singular Value Decomposition (SVD)
  • 8.3. Principal Component Analysis (PCA)
  • 8.4. Independent Component Analysis (ICA)
  • 8.5. Nonnegative matrix factorization (NMF)
• 9. Regression Algorithm
  • 9.1. Introduction
  • 9.2. Ordinary Least Squares Regression (OLSR)
    • 9.2.1. How to solve it?
    • 9.2.2. Ordinary Least Squares
  • 9.3. Linear Regression (LR)
• 10. Classification ALgorithms
  • 10.1. Logistic Regression (LR)
  • 10.2. k-Nearest Neighbour (kNN)
  • 10.3. Linear Discriminant Analysis (LDA)
  • 10.4. Quadratic Discriminant Analysis (QDA)
• 11. Regularization ALgorithms
  • 11.1. Subset Selection (SubS)
  • 11.2. Ridge Regression (Ridge)
  • 11.3. Least Absolute Shrinkage and Selection Operator (lASSO)
• 12. Resampling Algorithms
• 13. Developing Your Own R Packages
• 14. Developing Your Own Python Packages
  • 14.1. Hierarchical Structure
  • 14.2. Set Up
  • 14.3. Requirements
  • 14.4. ReadMe