Techniques for feature design to maximize model performance
Series Overview
This series is a practical educational content consisting of 4 chapters that progressively teaches Feature Engineering from the basics.
Feature Engineering is one of the most important processes that determines the performance of machine learning models. By appropriately preprocessing raw data and designing meaningful features, you can dramatically improve the prediction accuracy of your models. You will systematically master essential techniques for practical work, from handling missing data, encoding categorical variables, to feature transformation and selection.
Features:
- ✅ From Basics to Practice: Systematic learning from data preprocessing fundamentals to advanced feature design
- ✅ Implementation-Focused: 35+ executable Python code examples, practical techniques
- ✅ Intuitive Understanding: Understanding the effects of each method through visualization
- ✅ scikit-learn Utilization: Latest implementation methods using industry-standard libraries
- ✅ Practice-Oriented: Best practices immediately applicable in real work
Total Learning Time: 80-100 minutes (including code execution and exercises)
How to Learn
Recommended Learning Order
For Beginners (completely new to feature engineering):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 (all chapters recommended)
- Time required: 80-100 minutes
For Intermediate Learners (with machine learning experience):
- Chapter 2 → Chapter 3 → Chapter 4
- Time required: 60-70 minutes
Strengthening Specific Topics:
- Categorical variable processing: Chapter 2 (focused learning)
- Feature selection: Chapter 4 (focused learning)
- Time required: 20-25 minutes/chapter
Chapter Details
Chapter 1: Data Preprocessing Basics
Difficulty: Beginner to Intermediate
Reading Time: 20-25 minutes
Code Examples: 10
Learning Content
- Missing Value Handling - Deletion, mean imputation, KNN imputation
- Outlier Handling - IQR method, Z-score method, Isolation Forest
- Normalization and Standardization - Min-Max normalization, standardization, Robust Scaler
- Scaling Method Selection - Appropriate methods based on data distribution
- Pipeline Construction - Automating processes with scikit-learn Pipeline
Learning Objectives
- ✅ Understand types of missing values and appropriate handling methods
- ✅ Detect and appropriately handle outliers
- ✅ Select scaling methods according to data distribution
- ✅ Construct preprocessing pipelines
- ✅ Understand the impact of preprocessing on model performance
Chapter 2: Categorical Variable Encoding
Difficulty: Intermediate
Reading Time: 20-25 minutes
Code Examples: 10
Learning Content
- One-Hot Encoding - Converting categories to binary vectors
- Label Encoding - Converting categories to integers
- Target Encoding - Using statistics of target variable
- Frequency Encoding - Encoding occurrence frequency
- Encoding Method Selection - Selection based on cardinality and purpose
Learning Objectives
- ✅ Understand types of categorical variables
- ✅ Distinguish between One-Hot Encoding and Label Encoding
- ✅ Understand techniques to prevent information leakage in Target Encoding
- ✅ Effectively handle high cardinality variables
- ✅ Utilize the category_encoders library
Chapter 3: Feature Transformation and Generation
Difficulty: Intermediate
Reading Time: 20-25 minutes
Code Examples: 9
Learning Content
- Polynomial Features - Capturing feature interactions
- Logarithmic Transformation - Normalizing skewed distributions
- Box-Cox Transformation - Improving data normality
- Binning (Discretization) - Dividing continuous values into intervals
- Date/Time Feature Extraction - Generating useful features from temporal information
Learning Objectives
- ✅ Capture non-linear patterns with polynomial features
- ✅ Normalize highly skewed distributions with logarithmic transformation
- ✅ Understand application conditions for Box-Cox transformation
- ✅ Divide continuous values into meaningful intervals with binning
- ✅ Extract periodicity and seasonality from date/time data
Chapter 4: Feature Selection
Difficulty: Intermediate
Reading Time: 25-30 minutes
Code Examples: 10
Learning Content
- Filter Methods - Selection based on statistical indicators (correlation coefficient, variance, chi-square test)
- Wrapper Methods - Model-based selection (RFE, forward selection, backward elimination)
- Embedded Methods - Selection during model training (Lasso, Tree-based)
- Combination with Dimensionality Reduction - Joint use of PCA and feature selection
- Practical Selection Strategies - Method selection based on data size and computational resources
Learning Objectives
- ✅ Quickly remove irrelevant features with Filter methods
- ✅ Find optimal feature subsets with RFE
- ✅ Automatically select features with Lasso
- ✅ Interpret feature importance to gain business insights
- ✅ Maximize model performance while preventing overfitting
Overall Learning Outcomes
Upon completing this series, you will acquire the following skills and knowledge:
Knowledge Level (Understanding)
- ✅ Explain the importance of feature engineering and its impact on model performance
- ✅ Understand each method of data preprocessing, encoding, transformation, and selection
- ✅ Explain the characteristics and appropriate use of each method
- ✅ Appropriately determine processing policies for missing values and outliers
- ✅ Understand the design philosophy of scikit-learn's Transformer and Pipeline
Practical Skills (Doing)
- ✅ Appropriately impute missing values and handle outliers
- ✅ Encode categorical variables with multiple methods
- ✅ Transform data with polynomial features and logarithmic transformation
- ✅ Select features with Filter, Wrapper, and Embedded methods
- ✅ Build reusable preprocessing flows with Pipeline
Application Ability (Applying)
- ✅ Design appropriate preprocessing strategies for new datasets
- ✅ Design features leveraging domain knowledge
- ✅ Improve model performance through feature engineering
- ✅ Optimize while balancing overfitting and computational cost
Prerequisites
To effectively learn this series, it is desirable to have the following knowledge:
Required (Must Have)
- ✅ Python Basics: Variables, functions, loops, conditional statements
- ✅ NumPy Basics: Array operations, basic mathematical functions
- ✅ Pandas Basics: DataFrame operations, data reading and processing
- ✅ Machine Learning Basics: Model training and evaluation flow
Recommended (Nice to Have)
- 💡 Statistics Basics: Mean, variance, correlation coefficient, distribution
- 💡 scikit-learn Basics: Model fit/predict, cross-validation
- 💡 Matplotlib/Seaborn: Data visualization basics
- 💡 Supervised Learning Experience: Implementation experience with regression/classification models
Recommended Prior Learning:
- 📚 - Basic machine learning concepts
- 📚 - How to use Pandas and NumPy
Technologies and Tools Used
Main Libraries
- scikit-learn 1.3+ - Preprocessing, feature transformation, feature selection
- pandas 2.0+ - Data manipulation and preprocessing
- NumPy 1.24+ - Numerical computation
- category_encoders 2.6+ - Advanced categorical encoding
- Matplotlib 3.7+ - Visualization
- seaborn 0.12+ - Statistical visualization
Development Environment
- Python 3.8+ - Programming language
- Jupyter Notebook / Lab - Interactive development environment
- Google Colab - Cloud environment (available for free)
Let's Get Started!
Are you ready? Start with Chapter 1 and master the techniques of feature engineering!
Chapter 1: Data Preprocessing Basics →
Next Steps
After completing this series, we recommend proceeding to the following topics:
Deep Dive Learning
- 📚 Automated Feature Engineering: Featuretools, TPOT, AutoML
- 📚 Time Series Features: Lag features, moving averages, seasonal decomposition
- 📚 Text Features: TF-IDF, Word2Vec, BERT embeddings
- 📚 Image Features: HOG, SIFT, feature extraction using deep learning
Related Series
- 🎯 - Ensemble learning and advanced methods
- 🎯 - Hyperparameter optimization
- 🎯 - SHAP, LIME, feature importance
Practical Projects
- 🚀 Real Estate Price Prediction - Comprehensive exercise on numerical and categorical features
- 🚀 Customer Churn Prediction - Time series features and encoding
- 🚀 Credit Scoring - Feature selection and interpretability
- 🚀 Demand Forecasting - Date/time features and seasonality
Update History
- 2025-10-21: v1.0 Initial release
Your journey into feature engineering starts here!