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src//train_baseline.py

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+import os
+import sys
+import pickle
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+from sklearn.calibration import CalibratedClassifierCV
+from sklearn.pipeline import Pipeline
+
+def train_baseline():
+    """
+    Trains a TF-IDF + CalibratedClassifierCV(LogisticRegression) pipeline
+    and saves it as models/ticket_classifier/sklearn_router.pkl.
+    """
+    print("Training TF-IDF + Logistic Regression baseline pipeline...")
+    
+    # Ensure working directory is project root
+    project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    os.chdir(project_root)
+    
+    data_path = os.path.join("data", "raw", "support_tickets.csv")
+    
+    categories = [
+        'billing', 'technical_support', 'account_management', 'feature_request',
+        'compliance_legal', 'onboarding', 'general_inquiry', 'churn_risk'
+    ]
+    cat_to_id = {cat: i for i, cat in enumerate(categories)}
+
+    # Generate some fallback synthetic data if CSV is not present
+    if os.path.exists(data_path):
+        print(f"Loading data from {data_path}...")
+        try:
+            df = pd.read_csv(data_path)
+            # Assuming columns 'text' and 'category' exist
+            if 'text' in df.columns and 'category' in df.columns:
+                # Filter data to only include these categories
+                df = df[df['category'].isin(categories)].copy()
+                df['label'] = df['category'].map(cat_to_id)
+                
+                texts = df['text'].dropna().astype(str).tolist()
+                labels = df['label'].tolist()
+            else:
+                raise ValueError("CSV missing 'text' or 'category' columns.")
+        except Exception as e:
+            print(f"Error reading CSV: {e}. Falling back to synthetic data.")
+            texts, labels = get_synthetic_data()
+    else:
+        print(f"{data_path} not found. Generating synthetic baseline data...")
+        texts, labels = get_synthetic_data()
+
+    print(f"Training on {len(texts)} samples across {len(set(labels))} categories...")
+    
+    # Create the pipeline
+    pipeline = Pipeline([
+        ('tfidf', TfidfVectorizer(max_features=5000, stop_words='english', ngram_range=(1, 2))),
+        ('clf', CalibratedClassifierCV(LogisticRegression(class_weight='balanced', max_iter=1000))),
+    ])
+
+    # Fit the pipeline
+    pipeline.fit(texts, labels)
+
+    # Save the model
+    out_dir = os.path.join("models", "ticket_classifier")
+    os.makedirs(out_dir, exist_ok=True)
+    out_path = os.path.join(out_dir, "sklearn_router.pkl")
+    
+    with open(out_path, 'wb') as f:
+        pickle.dump(pipeline, f)
+    
+    print(f"Baseline model successfully saved to {out_path}")
+
+def get_synthetic_data():
+    """Returns synthetic data for fallback training."""
+    categories = [
+        'billing', 'technical_support', 'account_management', 'feature_request',
+        'compliance_legal', 'onboarding', 'general_inquiry', 'churn_risk'
+    ]
+    base_texts = {
+        'billing': ["invoice is wrong", "charge on my card", "cancel subscription", "refund request", "pricing plan"],
+        'technical_support': ["server is down", "cannot login", "getting 500 error", "bug in the app", "export failing"],
+        'account_management': ["change password", "update email", "delete account", "add user", "role permissions"],
+        'feature_request': ["add a feature", "new capability", "implement this", "suggest an improvement"],
+        'compliance_legal': ["gdpr report", "data handling documentation", "signed agreement", "privacy policy"],
+        'onboarding': ["setup help", "first time user", "getting started", "onboarding walkthrough"],
+        'general_inquiry': ["how do I", "question about", "more info", "demo please"],
+        'churn_risk': ["I am leaving", "cancel my account", "switching to competitor", "terrible service"]
+    }
+    
+    texts = []
+    labels = []
+    
+    # Create a reasonably sized synthetic dataset
+    for cat in categories:
+        for _ in range(50):
+            for text in base_texts[cat]:
+                texts.append(text)
+                labels.append(categories.index(cat))
+                
+    return texts, labels
+
+if __name__ == "__main__":
+    train_baseline()