# AI/ML Data Science Plugins

This directory contains 25 specialized plugins for machine learning, data science, and AI development workflows.

## Plugin Categories

### Model Development (1-6)

- **ml-model-trainer** - Train and optimize ML models
- **neural-network-builder** - Build neural network architectures
- **hyperparameter-tuner** - Optimize hyperparameters
- **classification-model-builder** - Build classification models
- **regression-analysis-tool** - Regression modeling
- **deep-learning-optimizer** - Deep learning optimization

### Data Processing (7-9)

- **data-preprocessing-pipeline** - Automated data preprocessing
- **feature-engineering-toolkit** - Feature creation and selection
- **dataset-splitter** - Split datasets for training/testing

### Domain-Specific ML (10-13)

- **nlp-text-analyzer** - Natural language processing
- **computer-vision-processor** - Computer vision tasks
- **time-series-forecaster** - Time series forecasting
- **recommendation-engine** - Recommendation systems

### Analysis & Detection (14-16)

- **anomaly-detection-system** - Detect anomalies in data
- **sentiment-analysis-tool** - Sentiment analysis
- **clustering-algorithm-runner** - Clustering algorithms

### MLOps & Production (17-20)

- **model-deployment-helper** - Deploy ML models
- **model-versioning-tracker** - Track model versions
- **experiment-tracking-setup** - Track ML experiments
- **data-visualization-creator** - Create data visualizations

### Advanced ML (21-25)

- **automl-pipeline-builder** - AutoML pipelines
- **transfer-learning-adapter** - Transfer learning
- **model-explainability-tool** - Model interpretability
- **model-evaluation-suite** - Comprehensive evaluation
- **ai-ethics-validator** - AI ethics and fairness validation

## Installation

All plugins can be installed from the claude-code-plugins-plus marketplace:

```bash
/plugin install ml-model-trainer@claude-code-plugins-plus
/plugin install data-preprocessing-pipeline@claude-code-plugins-plus
# ... etc
```

## Requirements

Most plugins require:

- Python 3.8+
- Common ML libraries (scikit-learn, pandas, numpy)
- Specific plugins may require PyTorch, TensorFlow, or other frameworks

Check individual plugin READMEs for specific dependencies.
