feature-engineering

name	feature-engineering
description	Procedural knowledge for transforming raw data into high-value features for machine learning models.

Skill: Feature Engineering

Procedural knowledge for transforming raw data into high-value features for machine learning models.

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Purpose

Enable the Data Scientist role to systematically clean, transform, and construct predictive features from raw data, ensuring maximum model performance while preventing data leakage.

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Prerequisites

• Exploratory Data Analysis (EDA) completed
• Target variable clearly defined
• Domain knowledge of the underlying data available

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Procedures

1. Data Cleaning & Imputation

Step 1: Handle Missing Values
- Determine the mechanism (Missing Completely At Random, Missing At Random, Not Missing At Random).
- Impute appropriately (mean, median, mode, predictive imputation) or drop.
- Create binary indicator columns for missingness if it carries signal.

Step 2: Outlier Treatment
- Identify outliers (Z-score, IQR).
- Cap/floor outliers or transform them (e.g., log transformation) to reduce their influence.

2. Feature Transformation & Creation

Step 1: Categorical Encoding
- Nominal data: One-Hot Encoding, Target Encoding.
- Ordinal data: Ordinal Encoding.

Step 2: Numerical Transformation
- Scaling/Standardization (MinMax, StandardScaler) for distance-based models or neural networks.
- Binning continuous variables into categories if relationships are highly non-linear.

Step 3: Domain-Specific Creation
- Extract date/time features (day of week, is_holiday).
- Create interaction terms (Feature A * Feature B).
- Aggregate historical data (e.g., "count of purchases in last 30 days").

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Common Pitfalls

1. Data Leakage: Applying scaling or imputation across the entire dataset before splitting into train/test sets.
2. Curse of Dimensionality: Creating too many features (e.g., massive One-Hot Encoded matrices) without feature selection.
3. Ignoring Time Linearity: Using future information to construct historical features in time-series data.

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Invocation

"Apply the feature-engineering skill from .ace/skills/feature-engineering/SKILL.md
to the raw customer behavior dataset."