📊 ml_anomaly_detector

A simple and extensible Dart package for anomaly detection using classical algorithms like Z-Score, with support for pluggable strategies and detailed result reporting.

🚀 Features

✅ Z-Score based anomaly detection
📦 Designed for extensibility (LOF, IQR, DBSCAN coming soon)
🔍 Returns detailed results: value, score, confidence, explanation
📊 Compatible with time-series or single-point anomaly detection
💡 Easy to use, production-ready

🧠 Example

import 'package:ml_anomaly_detector/ml_anomaly_detector.dart';

void main() {
  final data = [1.0, 1.1, 0.9, 1.2, 10.0];
  final detector = ZScoreDetector(threshold: 2.0);
  final results = detector.detect(data);

  for (var result in results) {
    print(
        'Value: \${result.value}, Score: \${result.score.toStringAsFixed(2)}, '
        'Anomaly: \${result.isAnomaly}');
  }
}

📦 Installation

Add the following line to your pubspec.yaml:

dependencies:
  ml_anomaly_detector: ^1.0.0

Then run:

dart pub get

📁 API Overview

`ZScoreDetector`

ZScoreDetector({
  double threshold = 3.0,
  String algorithmName = 'z_score',
});

`AnomalyResult`

Field	Type	Description
`value`	`double`	Original input value
`score`	`double`	Anomaly score (Z-score, etc.)
`isAnomaly`	`bool`	Whether it's an anomaly
`index`	`int?`	Optional index in dataset
`confidence`	`double?`	Confidence level (0.0 to 1.0)
`algorithm`	`String?`	Name of algorithm used
`explanation`	`String?`	Human-readable decision explanation

🧪 Tests

Run unit tests using:

dart test

📌 Roadmap

x Z-Score detector
IQR-based anomaly detection
Local Outlier Factor (LOF)
DBSCAN
Visual anomaly plotting (with Flutter)

📃 License

Contributions are welcome! PRs & feedback appreciated.