- Missing Values Imputation
- Handling Imbalanced Dataset
- SMOTE
- Handling Outliers
- Encoding (Nominal, One-Hot, Label, Ordinal)
| Model | Type | Learning Technique | Notes |
|---|---|---|---|
| Linear Regression (Custom) | Simple Regression | Analytical (OLS) | Fits line using closed-form solution; interpretable coefficients |
| Multiple Linear Regression | Multivariate | Analytical (OLS) | Supports multiple features; intercept & beta via matrix algebra |
| Polynomial Linear Regression | Univariate/Multivariate | Analytical (OLS) | Transforms features into polynomial basis; then applies OLS |
| Batch Gradient Descent | Multivariate | Iterative Optimization | Uses all data per step; stable but slower on large datasets |
| Stochastic Gradient Descent | Multivariate | Online Optimization | Updates weights per sample; faster but noisy |
| Mini-batch Gradient Descent | Multivariate | Hybrid Optimization | Compromise between BGD and SGD; faster convergence + smoother updates |
| Model | Penalty Type | Learning Technique | Notes |
|---|---|---|---|
| Ridge Regression | L2 | Analytical / Gradient | Penalizes large weights; shrinks coefficients; doesn't zero them out |
| Lasso Regression | L1 | Subgradient / Coordinate Descent | Encourages sparsity; can set coefficients exactly to zero (feature selection) |
| Elastic Net Regression | L1 + L2 | Coordinate Descent | Combines benefits of Ridge and Lasso; balances sparsity and stability |
✅ Use Cases:
- Ridge: When all features are useful
- Lasso: When only a few features are important (feature selection)
- Elastic Net: When groups of correlated features are present or hybrid behavior is desired
| Model | Activation | Learning Technique | Notes |
|---|---|---|---|
| Perceptron (Basic) | Step | Randomized update | Binary output (0 or 1), no probabilities |
| Perceptron (Sigmoid-based) | Sigmoid | Gradient-like updates | Adds non-linearity, smooth decision making |
| Logistic Regression (Custom) | Sigmoid | MLE + Gradient Descent | Trained using log loss; comparable to scikit-learn |
| Multiclass Logistic Regression | Softmax | Cross-Entropy + Gradient Descent | Load_Digits dataset, NDVI Land Cover Classification task |
- Dimensionality Reduction
- Principal Component Analysis (PCA)
- Clustering
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN Clustering
- Silhouette Score for Cluster Evaluation
- Anomaly Detection
- Isolation Forest
- DBSCAN Outlier Detection
- Local Outlier Factor (LOF)