refactor: Moved ML Model API Integration under RESTMessageV2 folder per guidelines

Author: BEASTSHRIRAM

Integration/RESTMessageV2/External ML Model Integration/Call ML Prediction API/README.md

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+# Integrate ServiceNow with External ML Model API
+
+## Overview
+Call an external ML API from ServiceNow to get AI predictions for incidents and auto-update records.
+
+## What It Does
+- Sends incident data to external ML API via REST call
+- Receives predictions (resolution time, category, priority, etc.)
+- Automatically updates incident record with predictions
+- Includes error handling and logging
+
+## Use Cases
+- Predict how long an incident will take to resolve
+- Auto-suggest the right category/priority
+- Recommend best assignment group
+- Get risk scores for changes
+
+## Files
+- `ml_prediction_script_include.js` - Script Include that calls ML API
+
+## How to Use
+1. Create Script Include in ServiceNow named `MLPredictionClient`
+2. Copy code from `ml_prediction_script_include.js`
+3. Update `ML_API_URL` and `API_KEY` with your ML service details
+4. Call it from a Business Rule or Client Script to get predictions
+5. Store results back in incident fields
+
+## Example Usage
+```javascript
+var mlClient = new MLPredictionClient();
+var prediction = mlClient.predictIncident({
+    description: incident.description,
+    category: incident.category,
+    priority: incident.priority
+});
+
+incident.estimated_resolution_time = prediction.predicted_resolution_time;
+incident.update();
+```
+
+## Requirements
+- ServiceNow instance
+- External ML API endpoint (REST)
+- API key or token

Integration/RESTMessageV2/External ML Model Integration/Call ML Prediction API/ml_prediction_script_include.js

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+// Script Include: MLPredictionClient
+// Calls external ML API to get incident predictions
+
+var MLPredictionClient = Class.create();
+MLPredictionClient.prototype = {
+    initialize: function() {
+        this.ML_API_URL = 'https://your-ml-api.com/predict';
+        this.API_KEY = 'your-api-key-here';
+    },
+    
+    predictIncident: function(incidentData) {
+        try {
+            var request = new RESTMessageV2();
+            request.setEndpoint(this.ML_API_URL);
+            request.setHttpMethod('POST');
+            request.setRequestHeader('Authorization', 'Bearer ' + this.API_KEY);
+            request.setRequestHeader('Content-Type', 'application/json');
+            
+            // Send incident details to ML API
+            var payload = {
+                description: incidentData.description,
+                category: incidentData.category,
+                priority: incidentData.priority
+            };
+            request.setRequestBody(JSON.stringify(payload));
+            
+            // Get prediction from external ML service
+            var response = request.execute();
+            var result = JSON.parse(response.getBody());
+            
+            return {
+                estimated_hours: result.estimated_hours,
+                predicted_category: result.category,
+                confidence: result.confidence
+            };
+        } catch (error) {
+            gs.log('ML API Error: ' + error, 'MLPredictionClient');
+            return null;
+        }
+    },
+    
+    type: 'MLPredictionClient'
+};