Pseudo Code for Feature Engineering

Overview

Feature engineering is the process of creating new features or modifying existing features to improve the performance of a machine learning model. Below is a structured approach to accomplish feature engineering.

Steps in Feature Engineering

1. Data Acquisition

• Load the dataset
• Inspect initial rows to understand structure

2. Data Preprocessing

• Handle missing values
  • If missing values exist:
    • Select a strategy (e.g., mean, median, mode, removal)
    • Apply selected strategy to fill missing values
• Convert categorical variables to numerical (if applicable)
  • For each categorical feature:
    • Use one-hot encoding or label encoding

3. Feature Selection

• Identify relevant features
  • Calculate correlation among features
  • Select features with high correlation to target variable
• Drop irrelevant or redundant features

4. Feature Transformation

• Normalize or standardize features
  • For each feature:
    • If using normalization: Scale values to [0, 1]
    • If using standardization: Scale values to have a mean of 0 and a standard deviation of 1
• Create new features from existing ones
  • For each feature:
    • Derive new features using mathematical transformations (e.g., logarithm, square root)

5. Feature Interaction

• Create interaction features
  • For each pair of features:
    • Calculate and add a new feature representing their interaction (e.g., multiplication)

6. Final Review

• Inspect the new feature set
• Validate the modifications made to the feature set

Pseudo Code Implementation

FUNCTION FeatureEngineering(data):
    // Step 1: Data Acquisition
    dataset = LoadData(data)
    PRINT "Initial Rows in Dataset:", ShowRows(dataset)

    // Step 2: Data Preprocessing
    IF MissingValuesExists(dataset):
        strategy = SelectImputationStrategy()
        ImputeMissingValues(dataset, strategy)

    ConvertCategoricalToNumerical(dataset)

    // Step 3: Feature Selection
    correlationMatrix = CalculateCorrelation(dataset)
    selectedFeatures = SelectRelevantFeatures(correlationMatrix)

    dataset = KeepOnlySelectedFeatures(dataset, selectedFeatures)

    // Step 4: Feature Transformation
    FOR each feature IN dataset:
        NormalizeOrStandardize(feature)

        // Create new features
        newFeature = DeriveNewFeature(feature)
        ADD newFeature TO dataset

    // Step 5: Feature Interaction
    FOR each feature1, feature2 IN dataset:
        interactionFeature = CreateInteractionFeature(feature1, feature2)
        ADD interactionFeature TO dataset

    // Step 6: Final Review
    PRINT "Final Feature Set:", ShowRows(dataset)
    RETURN dataset

Conclusion

This pseudo code outlines the critical steps to perform feature engineering in a structured manner. It covers data acquisition, preprocessing, feature selection, transformation, interaction, and final review. By following this guide, one can systematically enhance the feature set to improve machine learning model performance.