6.2.3. Key Concepts Review: EDA & Feature Engineering

First Principle: Effective Exploratory Data Analysis (EDA) and rigorous Feature Engineering fundamentally transform raw data into a high-quality, informative representation, uncovering patterns, mitigating issues, and providing the optimal input for machine learning algorithms.

This review consolidates concepts for EDA and Feature Engineering.

Core Concepts & AWS Services for EDA & Feature Engineering:

• Data Cleaning and Preprocessing:
  • Handling Missing Values: Deletion (listwise, pairwise), Imputation (mean, median, mode, regression-based).
  • Handling Outliers: Detection (Z-score, IQR, Isolation Forest), Treatment (capping, transformation, binning, deletion).
  • Data Type Conversion & Normalization/Standardization: Min-Max Scaling, Z-score standardization.
  • Text Preprocessing: Lowercasing, punctuation/stop word removal, tokenization, stemming, lemmatization.
  • Tools: SageMaker Data Wrangler, SageMaker Processing Jobs, Glue ETL Jobs.
• Data Visualization and Statistical Analysis:
  • Purpose: Understand distributions, relationships, issues.
  • Statistical Methods: Central tendency (mean, median), dispersion (std dev, IQR), skewness, kurtosis, frequency, hypothesis testing.
  • Tools: SageMaker Notebooks (Python libs), Athena (SQL), QuickSight (BI dashboards), SageMaker Data Wrangler.
  • Correlation Analysis & Feature Importance: Pearson correlation, model-based importance (XGBoost), SHAP/LIME.
• Feature Engineering Techniques:
  • Categorical Encoding: One-Hot, Label, Target Encoding.
  • Numerical Transformations: Log, Polynomial, Binning.
  • Time-Series Feature Engineering: Lag features, rolling aggregations, date/time components.
  • Feature Store: Online/Offline store for consistent feature serving (SageMaker Feature Store).
• Handling Data Imbalance: Sampling (Oversampling, Undersampling, SMOTE), cost-sensitive learning.

Scenario: You have a dataset of raw sensor readings that needs to be cleaned, transformed into time-series features, and then analyzed for patterns and relationships before feeding into an anomaly detection model.

Reflection Question: How do Exploratory Data Analysis (EDA) (using tools like SageMaker Notebooks for visualization and statistical analysis) and Feature Engineering (applying techniques like log transformation, one-hot encoding, and leveraging a Feature Store) fundamentally transform raw data into a high-quality, informative representation, optimizing it for ML model consumption?