Add POI feature and imporve the overall backend implementation

services/training/README.md

@@ -38,7 +38,8 @@ The **Training Service** is the machine learning pipeline engine of Bakery-IA, r
 ### Feature Engineering
 - **Historical Data Aggregation** - Collect sales data for model training
 - **External Data Integration** - Fetch weather, traffic, holiday data
-- **Feature Extraction** - Generate 20+ temporal and contextual features
+- **POI Feature Integration** - Merge location-based POI features into training data
+- **Feature Extraction** - Generate 30+ temporal, contextual, and location-based features
 - **Data Validation** - Ensure minimum data requirements before training
 - **Outlier Detection** - Filter anomalous data points
 
@@ -61,6 +62,10 @@ async def train_model_pipeline(tenant_id: str, product_id: str):
     weather_data = await fetch_weather_data(tenant_id)
     traffic_data = await fetch_traffic_data(tenant_id)
     holiday_data = await fetch_holiday_calendar()
+    poi_features = await fetch_poi_features(tenant_id)  # NEW: Location context
+
+    # Merge POI features into training dataframe
+    features = merge_poi_features(features, poi_features)
 
     # Step 3: Prophet Model Training
     model = Prophet(
@@ -533,11 +538,56 @@ async def test_training_progress():
 asyncio.run(test_training_progress())
 ```
 
+## POI Feature Integration
+
+### How POI Features Enhance Training
+
+The Training Service integrates location-based POI features from the External Service to improve forecast accuracy:
+
+**POI Features Included:**
+- `school_density` - Number of schools within 1km radius
+- `office_density` - Number of offices and business centers nearby
+- `residential_density` - Residential area proximity
+- `transport_hub_proximity` - Distance to metro, bus, train stations
+- `commercial_zone_score` - Commercial activity in the area
+- `restaurant_density` - Nearby restaurants and cafes
+- `competitor_proximity` - Distance to competing bakeries
+- And 11+ more location-based features
+
+**Integration Process:**
+1. **Fetch POI Context** - Retrieve tenant's POI features from External Service (`/poi-context/{tenant_id}`)
+2. **Extract ML Features** - Parse `ml_features` JSON object from POI context
+3. **Merge with Training Data** - Add POI features as additional columns in training dataframe
+4. **Prophet Training** - Include POI features as regressors in Prophet model
+5. **Feature Importance** - Track which POI features most impact predictions
+
+**Example POI Feature Integration:**
+```python
+from app.ml.poi_feature_integrator import POIFeatureIntegrator
+
+# Initialize POI integrator
+poi_integrator = POIFeatureIntegrator(external_service_url)
+
+# Fetch and merge POI features
+poi_features = await poi_integrator.fetch_poi_features(tenant_id)
+training_df = poi_integrator.merge_poi_features(training_df, poi_features)
+
+# POI features now available as columns:
+# training_df['school_density'], training_df['office_density'], etc.
+
+# Add POI features as Prophet regressors
+for feature_name in poi_features.keys():
+    prophet_model.add_regressor(feature_name)
+```
+
+**Endpoint Used:**
+- `GET {EXTERNAL_SERVICE_URL}/poi-context/{tenant_id}` - Fetch POI features
+
 ## Integration Points
 
 ### Dependencies (Services Called)
 - **Sales Service** - Fetch historical sales data for training
-- **External Service** - Fetch weather, traffic, holiday data
+- **External Service** - Fetch weather, traffic, holiday, and POI feature data
 - **PostgreSQL** - Store job queue, models, metrics, logs
 - **RabbitMQ** - Publish training completion events
 - **File System** - Store model artifacts