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Computer Science
Artificial Intelligence
Machine Learning
Feature Engineering for Machine Learning
1. Introduction to Feature Engineering
2. Foundational Concepts
3. Exploratory Data Analysis for Features
4. Data Cleaning and Preparation
5. Feature Scaling and Normalization
6. Categorical Feature Engineering
7. Feature Creation and Generation
8. Temporal Feature Engineering
9. Text Feature Engineering
10. Geospatial Feature Engineering
11. Feature Selection Methods
12. Dimensionality Reduction
13. Advanced Feature Engineering
14. Evaluation and Validation
15. Implementation and Best Practices
16. Common Pitfalls and Solutions
8.
Temporal Feature Engineering
8.1.
DateTime Component Extraction
8.1.1.
Year, Month, Day
8.1.2.
Hour, Minute, Second
8.1.3.
Day of Week
8.1.4.
Day of Year
8.1.5.
Week of Year
8.1.6.
Quarter
8.1.7.
Season
8.1.8.
Is Weekend
8.1.9.
Is Holiday
8.1.10.
Is Month End
8.1.11.
Is Month Start
8.2.
Time-Based Calculations
8.2.1.
Time Since Event
8.2.2.
Time Until Event
8.2.3.
Duration Between Events
8.2.4.
Age Calculations
8.2.5.
Tenure Features
8.3.
Lag Features
8.3.1.
Single Lag Features
8.3.2.
Multiple Lag Features
8.3.3.
Seasonal Lags
8.3.4.
Variable Lag Windows
8.4.
Rolling Window Features
8.4.1.
Moving Averages
8.4.2.
Rolling Statistics
8.4.2.1.
Rolling Sum
8.4.2.2.
Rolling Min
8.4.2.3.
Rolling Max
8.4.2.4.
Rolling Standard Deviation
8.4.2.5.
Rolling Median
8.4.3.
Exponentially Weighted Features
8.4.4.
Custom Rolling Functions
8.5.
Time Series Decomposition
8.5.1.
Trend Components
8.5.2.
Seasonal Components
8.5.3.
Residual Components
8.5.4.
Cyclical Patterns
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9. Text Feature Engineering