Upload src\train_sla.py with huggingface_hub

src//train_sla.py

@@ -0,0 +1,55 @@
+# src/train_sla.py
+# Train XGBoost model for SLA Breach Prediction
+
+import os
+import pandas as pd
+import xgboost as xgb
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+DATA_DIR = os.path.join(BASE_DIR, 'data', 'processed')
+MODEL_DIR = os.path.join(BASE_DIR, 'models', 'sla_predictor')
+MODEL_PATH = os.path.join(MODEL_DIR, 'sla_xgb.json')
+
+FEATURE_NAMES = [
+    'text_complexity_score', 'agent_queue_depth', 'customer_tier',
+    'hour_of_day', 'day_of_week', 'similar_ticket_avg_hrs',
+    'sentiment_score', 'repeat_issue', 'escalated_before'
+]
+
+def main():
+    data_path = os.path.join(DATA_DIR, 'sla_train.csv')
+    if not os.path.exists(data_path):
+        logger.error(f"SLA training data not found at {data_path}. Run prepare_kaggle_data.py first.")
+        return
+
+    logger.info("Loading SLA training data...")
+    df = pd.read_csv(data_path)
+    
+    X = df[FEATURE_NAMES]
+    y = df['sla_breached']
+    
+    logger.info("Training XGBoost SLA Predictor...")
+    dtrain = xgb.DMatrix(X, label=y, feature_names=FEATURE_NAMES)
+    
+    params = {
+        'objective': 'binary:logistic',
+        'eval_metric': 'auc',
+        'max_depth': 6,
+        'eta': 0.1,
+        'subsample': 0.8,
+        'colsample_bytree': 0.8,
+        'seed': 42
+    }
+    
+    model = xgb.train(params, dtrain, num_boost_round=100)
+    
+    os.makedirs(MODEL_DIR, exist_ok=True)
+    model.save_model(MODEL_PATH)
+    logger.info(f"SLA Model saved successfully to {MODEL_PATH}")
+
+if __name__ == "__main__":
+    main()