CONTENTS
Chapter 1 Basic Concepts
Chapter 2 Automatic Data Analysis
- 2.1 Data preparation and import
- 2.2 Identify data type
- 2.3 Quantitative data analysis
- 2.4 Qualitative data analysis
- 2.5 Correlation analysis
Chapter 3 Data Preprocessing
- 3.1 Fully automated preprocessing
- 3.2 Introduction to preprocessing methods
- Variable preliminary filtering
- Outlier handling
- Missing value handling
- Categorical variable handling
- Time variable processing
- Skewness handling
- Balanced sampling
- Data standardization
- Dataset split
Chapter 4 Modeling
- 4.1 Supervised learning
- 4.2 One-click modeling
- 4.3 Algorithm description
- Linear model
- Tree model
- Ensemble learning
- Deep learning
Chapter 5 Model evaluation
- 5.1 Classification model evaluation
- Confusion matrix
- Accuracy table
- ROC, AUC
- Gini, KS
- Lift graph
- Recall chart
- 5.2 Regression model evaluation
- Model error evaluation
- Residual plot
- Result comparison graph
Chapter 6 Model tuning
- 6.1 Derived variable
- Binning
- Feature variable self-transformation
- Variable transformation align with target
- Variable interaction
- Ratio
- Date time variable
- Other derivatives
- 6.2 Algorithm selection and parameter tuning
- Algorithm selection
- Parameter tuning
- 6.3 Appendix - Common algorithm parameters
Chapter 7 Comprehensive cases
- 7.1 Classification model case
- 7.2 Regression model case