Create TODO.md

Author: ObservedObserver

TODO.md

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+# Frontend ML Algorithms Package - Todo List
+
+## Priority 1: Core Foundation (Essential)
+
+### Linear Models
+- [x] Linear Regression (simple & multiple)
+- [x] Logistic Regression
+- [ ] Polynomial Regression
+
+### Basic Classification
+- [x] k-Nearest Neighbors (k-NN)
+- [ ] Naive Bayes
+- [x] Decision Trees
+
+### Clustering
+- [x] k-Means Clustering
+- [x] k-Means++ (improved initialization)
+- [ ] Hierarchical Clustering
+
+### Utilities & Preprocessing
+- [ ] Data normalization/standardization
+- [ ] Train/test split functionality
+- [ ] Cross-validation
+- [ ] Feature scaling
+- [ ] Missing data handling
+
+### Distance Metrics & Evaluation
+- [x] Manhattan Distance
+- [x] Euclidean Distance  
+- [x] Minkowski Distance
+- [x] Accuracy Score
+
+## Priority 2: Intermediate Algorithms (Important)
+
+### Ensemble Methods
+- [ ] Random Forest
+- [ ] Bagging
+- [ ] Boosting (AdaBoost)
+
+### Advanced Classification
+- [x] Support Vector Machines (SVM)
+- [x] Linear SVM
+- [x] Nu-SVM
+- [ ] Multi-class classification wrappers
+
+### Dimensionality Reduction
+- [x] Principal Component Analysis (PCA)
+- [ ] Linear Discriminant Analysis (LDA)
+
+### Regression Variants
+- [ ] Ridge Regression
+- [ ] Lasso Regression
+- [ ] Elastic Net
+
+### Clustering Extensions
+- [x] DBSCAN
+- [x] HDBSCAN (Hierarchical DBSCAN)
+- [x] OPTICS (Ordering Points To Identify Clustering Structure)
+- [ ] Gaussian Mixture Models
+
+## Priority 3: Specialized Algorithms (Nice to Have)
+
+### Time Series
+- [ ] Moving Averages
+- [ ] Exponential Smoothing
+- [ ] ARIMA (simplified)
+- [ ] Seasonal decomposition
+
+### Association Rules
+- [ ] Apriori Algorithm
+- [ ] Market Basket Analysis
+
+### Anomaly Detection
+- [x] Isolation Forest
+- [ ] One-Class SVM
+- [ ] Local Outlier Factor (LOF)
+
+### Advanced Clustering
+- [ ] Spectral Clustering
+- [x] Mean Shift
+
+## Priority 4: Neural Networks & Deep Learning (Advanced)
+
+### Basic Neural Networks
+- [ ] Perceptron
+- [ ] Multi-layer Perceptron (MLP)
+- [ ] Backpropagation implementation
+
+### Specialized Networks
+- [ ] Convolutional Neural Network (CNN) - basic
+- [ ] Recurrent Neural Network (RNN) - basic
+- [ ] Long Short-Term Memory (LSTM) - simplified
+
+## Priority 5: Optimization & Advanced Features
+
+### Optimization Algorithms
+- [ ] Gradient Descent variants
+- [ ] Genetic Algorithm
+- [ ] Particle Swarm Optimization
+
+### Model Selection
+- [ ] Grid Search
+- [ ] Random Search
+- [ ] Feature selection algorithms
+
+### Advanced Preprocessing
+- [ ] Text preprocessing utilities
+- [ ] Image preprocessing utilities
+- [ ] Feature engineering helpers
+
+## Implementation Considerations
+
+### Core Infrastructure
+- [ ] Modular architecture design
+- [ ] TypeScript definitions
+- [ ] Unit testing framework
+- [ ] Performance benchmarking
+- [ ] Memory optimization
+- [ ] WebWorker support for heavy computations
+
+### Data Handling
+- [ ] Matrix operations library
+- [ ] CSV/JSON data loaders
+- [ ] Data visualization helpers
+- [ ] Export/import model functionality
+
+### Documentation & Examples
+- [ ] API documentation
+- [ ] Interactive examples
+- [ ] Performance comparisons
+- [ ] Browser compatibility testing
+
+## Recommended Implementation Order
+
+**Phase 1 (MVP)**: Complete Priority 1 items - these provide the foundation for most ML tasks and will make your package immediately useful.
+
+**Phase 2 (Growth)**: Add Priority 2 algorithms - these significantly expand capabilities while building on the foundation.
+
+**Phase 3 (Specialization)**: Implement Priority 3 based on user feedback and specific use cases.
+
+**Phase 4 (Advanced)**: Neural networks require significant complexity - consider if simpler alternatives or integration with existing libraries (like TensorFlow.js) might be better.
+
+**Phase 5 (Polish)**: Advanced features and optimizations to make the package production-ready.
+
+## Notes
+- Focus on algorithms that perform well in browser environments
+- Consider memory constraints and computation limits
+- Prioritize algorithms with broad applicability
+- Ensure good performance with moderate dataset sizes (< 10k samples typically)