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USAGE_EXAMPLE.md

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+
+# Usage Example
+
+## Installation
+```bash
+pip install huggingface_hub
+```
+
+## Loading the Model
+```python
+from huggingface_hub import hf_hub_download
+import pickle
+
+# Download and load the model
+model_path = hf_hub_download(
+    repo_id="RayyanAhmed9477/house-price-prediction-model",
+    filename="house_price_model.pkl"
+)
+
+with open(model_path, 'rb') as f:
+    model_data = pickle.load(f)
+```
+
+## Making Predictions
+```python
+import pandas as pd
+
+# Prepare input data
+input_data = pd.DataFrame([{
+    "property_type": "House",
+    "location": "DHA Defence", 
+    "city": "Lahore",
+    "baths": 3,
+    "purpose": "For Sale",
+    "bedrooms": 4,
+    "Area_in_Marla": 5.0
+}])
+
+# Encode categorical variables
+for col in ["property_type", "location", "city", "purpose"]:
+    if col in model_data["label_encoders"]:
+        le = model_data["label_encoders"][col]
+        try:
+            input_data[col] = le.transform([str(input_data[col].iloc[0])])
+        except ValueError:
+            # Handle unknown categories
+            input_data[col] = le.transform([le.classes_[0]])
+
+# Make prediction
+prediction = model_data["model"].predict(input_data[model_data["feature_columns"]])[0]
+print(f"Predicted Price: {prediction}")
+```
+