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Data & Analytics
Data Preparation for Machine Learning: Best Practices
Master data cleaning, feature engineering, and preprocessing techniques for better ML models.
Ankit 14 min readNovember 26, 2024
The Foundation of ML
Data preparation typically takes 60-80% of ML project time. Quality preparation directly impacts model performance.
Data Quality Issues
Missing Values
- Types: MCAR, MAR, MNAR
- Detection methods
- Handling strategies
Outliers
- Statistical detection
- Domain-based rules
- Handling approaches
Inconsistencies
- Format variations
- Duplicate detection
- Data conflicts
Cleaning Techniques
Missing Data
- Deletion (listwise, pairwise)
- Imputation (mean, median, mode)
- Advanced (KNN, model-based)
Outlier Handling
- Removal
- Capping
- Transformation
- Robust methods
Normalization
- Min-max scaling
- Standardization
- Log transformation
Feature Engineering
Numerical Features
- Binning
- Polynomial features
- Interactions
Categorical Features
- One-hot encoding
- Target encoding
- Embeddings
Text Features
- Tokenization
- TF-IDF
- Embeddings
Date/Time
- Components extraction
- Cyclical encoding
- Lag features
Data Splitting
- Train/validation/test
- Cross-validation
- Time-based splits
- Stratification
Tools
- Pandas
- scikit-learn
- Feature-engine
- Great Expectations
Conclusion
Thorough data preparation is essential for building effective ML models.
Data PreparationFeature EngineeringData Quality
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