Predictive Analytics

  1. Regression Modeling
    1. Linear Regression Fundamentals
      1. Simple Linear Regression
        1. Mathematical Foundation
          1. Least Squares Method
            1. Model Assumptions
              1. Coefficient Interpretation
                1. Residual Analysis
                2. Multiple Linear Regression
                  1. Model Specification
                    1. Matrix Formulation
                      1. Coefficient Estimation
                        1. Statistical Inference
                        2. Regression Diagnostics
                          1. Linearity Assessment
                            1. Independence Testing
                              1. Homoscedasticity Evaluation
                                1. Normality of Residuals
                                  1. Multicollinearity Detection
                                    1. Variance Inflation Factor
                                      1. Condition Index
                                        1. Correlation Matrix Analysis
                                    2. Advanced Linear Models
                                      1. Polynomial Regression
                                        1. Polynomial Feature Creation
                                          1. Degree Selection
                                            1. Overfitting Prevention
                                            2. Interaction Models
                                              1. Two-way Interactions
                                                1. Higher-order Interactions
                                                  1. Interaction Interpretation
                                                  2. Piecewise Regression
                                                    1. Breakpoint Identification
                                                      1. Spline Regression
                                                        1. Threshold Models
                                                      2. Regularized Regression
                                                        1. Ridge Regression
                                                          1. L2 Penalty Function
                                                            1. Shrinkage Effects
                                                              1. Lambda Parameter Tuning
                                                              2. Lasso Regression
                                                                1. L1 Penalty Function
                                                                  1. Feature Selection Properties
                                                                    1. Sparse Solutions
                                                                    2. Elastic Net
                                                                      1. Combined L1 and L2 Penalties
                                                                        1. Alpha Parameter Selection
                                                                          1. Mixing Parameter Optimization
                                                                          2. Regularization Path Analysis
                                                                            1. Cross-validation for Parameter Selection
                                                                              1. Regularization Strength Effects
                                                                            2. Non-linear Regression
                                                                              1. Generalized Linear Models
                                                                                1. Exponential Family Distributions
                                                                                  1. Maximum Likelihood Estimation
                                                                                  2. Kernel Regression
                                                                                    1. Kernel Functions
                                                                                      1. Bandwidth Selection
                                                                                        1. Local Polynomial Regression
                                                                                        2. Regression Trees
                                                                                          1. Tree Construction
                                                                                            1. Pruning Strategies
                                                                                              1. Ensemble Applications

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                                                                                          2. Data Foundation and Preparation

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                                                                                          4. Classification Modeling