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1.
Introduction
2.
Machine Learning
2.1
Linear Regression
2.1.1 Least-squares method
2.1.2 Ridge and Lasso regression
2.1.3 Outliers and Robustness
2.2
Linear Classification
2.2.1 Logistic Regression
2.2.2 Linear Discriminant Analysis
2.3
Decision Tree
2.3.1 Decision Tree (Classification)
2.3.2 Decision tree (regression)
2.3.3 Decision Tree Parameters
2.4
Ensemble
2.4.1 Random Forests
2.4.2 Stacking
2.4.3 Adaboost (classification)
2.4.4 Adaboost(Regression)
2.4.5 Gradient boosting
2.5
Clustering
2.5.1 k-means
2.5.2 k-means++
2.5.3 X-means
2.6
Dimensionality Reduction
2.6.1 PCA
2.6.2 SVD
2.7
Feature Selection
2.8
Time Series
2.8.1 Using Prophet
2.9
Anomaly detection
2.9.1 Anomaly Detection ①
2.9.2Anomaly Detection②
3.
Preprocess
3.1
Numerical Data
3.3.1 Binning
3.3.2 BoxCox transformation
3.3.3 YeoJonson transformation
3.2
Categorical Data
3.3
Table
3.4
Others
4.
Metrics
4.1
Model Selection
4.2
Regression
4.2.1 Correlation coefficient
4.2.2 Coefficient of determination
4.3
Classification
4.3.1 ROC-AUC
5.
TimeSeries
5.1
Plotting and Preprocessing
5.1.1
Check Dataset
5.1.2
Impact of Trends
5.1.3
Trend & Periodicity
5.1.4
Box-Cox transformation
5.1.5
Adjustment
5.2
Exponential smoothing
5.3
Univariate
5.3.5
AR Process
5.3.6
MA Process
5.4
Multi-variate
5.5
Shape & Similarity
5.5.1 Dynamic Time Warping
5.6
timeseries forecast
5.7
Hierarchical and Grouped Time Series
6.
Visualization
6.1
Numeric Value Distribution
6.1.1 Map of Japan
6.1.2 Tree map
6.1.3 Doughnut chart
6.1.4 Sankey Diagram
6.2
Category & Number
6.2.1 Histogram
6.2.2 Density plot
6.2.3 Ridgeline plot
6.2.4 Violin plot
Appendix
7.
Economic Data
7.1
Time Series
7.1.1
FRED
7.1.2 mplfinance
7.2
Visualize
7.2.1 Country risk premium
7.2.2 positive and negative changes
7.2.3 Radar chart
7.3
NLP
7.3.1 Sentiment analysis of text
Issues
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Chapter 9
Anomaly detection
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