Feature Engineering for Machine Learning

  1. Feature Scaling and Normalization
    1. Scaling Fundamentals
      1. Need for Scaling
        1. Algorithm Sensitivity
          1. Distance-Based Methods
            1. Gradient Descent Optimization
              1. Feature Magnitude Differences
              2. Scale-Invariant vs Scale-Dependent Algorithms
              3. Standardization Techniques
                1. Z-Score Standardization
                  1. Robust Standardization
                    1. Unit Vector Scaling
                      1. Max-Abs Scaling
                      2. Normalization Methods
                        1. Min-Max Normalization
                          1. Decimal Scaling
                            1. Vector Normalization
                            2. Distribution Transformation
                              1. Log Transformation
                                1. Square Root Transformation
                                  1. Reciprocal Transformation
                                    1. Box-Cox Transformation
                                      1. Yeo-Johnson Transformation
                                        1. Quantile Transformation
                                          1. Power Transformations
                                          2. Discretization and Binning
                                            1. Equal Width Binning
                                              1. Equal Frequency Binning
                                                1. K-Means Binning
                                                  1. Decision Tree Binning
                                                    1. Custom Binning Strategies
                                                      1. Optimal Binning Methods

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                                                    4. Data Cleaning and Preparation

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                                                    6. Categorical Feature Engineering