initial commit

backend/app/services/ml_model.py

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+import random
+from typing import List, Tuple
+
+class HousePricePredictor:
+    """
+    Mock ML model that predicts house prices.
+    In a real scenario, this would load a trained model.
+    """
+    
+    def __init__(self):
+        # Mock initialization - in reality would load model weights
+        pass
+    
+    def predict(self, square_feet: float, bedrooms: int, bathrooms: float) -> Tuple[float, float, List[float]]:
+        """
+        Mock prediction method that returns:
+        - predicted price
+        - confidence score
+        - similar listing prices
+        """
+        # Mock prediction logic
+        base_price = square_feet * 200
+        bedroom_value = bedrooms * 25000
+        bathroom_value = bathrooms * 15000
+        
+        predicted_price = base_price + bedroom_value + bathroom_value
+        
+        # Add some randomness to make it interesting
+        confidence_score = random.uniform(0.8, 0.99)
+        similar_listings = [
+            predicted_price * random.uniform(0.9, 1.1) 
+            for _ in range(3)
+        ]
+        
+        return predicted_price, confidence_score, similar_listings