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Data Outlier Processor

Advanced outlier detection and processing tool that identifies, removes, or replaces anomalous values in numerical data using multiple statistical methods. Perfect for data cleaning, statistical analysis, and machine learning data preparation. Features: - Multiple detection methods (IQR, Z-score, Modified Z-score, Isolation Forest) - Flexible handling strategies (Remove, Replace with mean/median/mode, Cap) - Automatic threshold optimization - Multi-dimensional outlier detection - Visual outlier statistics and reporting - Batch processing capabilities - Custom sensitivity levels - Comprehensive impact analysis Common Use Cases: - Data cleaning and preprocessing - Statistical analysis preparation - Machine learning dataset cleaning - Quality control in manufacturing - Financial anomaly detection - Sensor data validation

Sensitivity threshold for outlier detection. Lower values detect more outliers.

Add columns to flag which values were detected as outliers

Automatically find optimal threshold based on data distribution

API Documentation

Request Endpoint

POST /en/api/tools/data-outlier-processor

Request Parameters

Parameter Name Type Required Description
inputData textarea Yes -
targetColumns textarea No -
detectionMethod select No -
threshold number No Sensitivity threshold for outlier detection. Lower values detect more outliers.
handlingStrategy select No -
replacementMethod select No -
preserveOriginal checkbox No -
markOutliers checkbox No Add columns to flag which values were detected as outliers
includeStatistics checkbox No -
autoThreshold checkbox No Automatically find optimal threshold based on data distribution
sensitivity select No -

Response Format

{
  "result": "Processed text content",
  "error": "Error message (optional)",
  "message": "Notification message (optional)",
  "metadata": {
    "key": "value"
  }
}
Text: Text

AI MCP Documentation

Add this tool to your MCP server configuration: 

{
  "mcpServers": {
    "elysiatools-data-outlier-processor": {
      "name": "data-outlier-processor",
      "description": "Advanced outlier detection and processing tool that identifies, removes, or replaces anomalous values in numerical data using multiple statistical methods. Perfect for data cleaning, statistical analysis, and machine learning data preparation.

Features:
- Multiple detection methods (IQR, Z-score, Modified Z-score, Isolation Forest)
- Flexible handling strategies (Remove, Replace with mean/median/mode, Cap)
- Automatic threshold optimization
- Multi-dimensional outlier detection
- Visual outlier statistics and reporting
- Batch processing capabilities
- Custom sensitivity levels
- Comprehensive impact analysis

Common Use Cases:
- Data cleaning and preprocessing
- Statistical analysis preparation
- Machine learning dataset cleaning
- Quality control in manufacturing
- Financial anomaly detection
- Sensor data validation",
      "baseUrl": "https://elysiatools.com/mcp/sse?toolId=data-outlier-processor",
      "command": "",
      "args": [],
      "env": {},
      "isActive": true,
      "type": "sse"
    }
  }
}

You can chain multiple tools, e.g.: `https://elysiatools.com/mcp/sse?toolId=png-to-webp,jpg-to-webp,gif-to-webp`, max 20 tools.

If you encounter any issues, please contact us at [email protected]