Update README.md

README.md

@@ -1,3 +1,51 @@
----
-license: mit
----
+---
+license: mit
+tags:
+  - regression
+  - xgboost
+  - house-price
+  - india
+  - real-estate
+  - machine-learning
+datasets:
+  - house-prices-india
+widget:
+  - source: https://huggingface.co/spaces/bryium/house_price
+    inference: false
+---
+
+# House Price Predictor (India)
+
+XGBoost model that predicts house prices in **Indian Rupees** using features like location, carpet area, number of bedrooms (BHK), furnishing, and more. Trained on Indian housing data with an R² of ~0.84.
+
+## Model Details
+
+| | |
+|---|---|
+| **Model type** | XGBoost Regressor |
+| **Task** | Regression (house price prediction) |
+| **Input** | Location, carpet area, BHK, furnishing, status, transaction, etc. |
+| **Output** | Predicted price in ₹ (Indian Rupees) |
+| **R²** | ~0.84 |
+| **RMSE** | ~0.21 (log scale) |
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `model.joblib` | Trained XGBRegressor |
+| `preprocessor.joblib` | Sklearn ColumnTransformer (imputer + scaler + OneHotEncoder) |
+| `encodings.joblib` | Target/frequency encodings for location & society |
+| `feature_columns.joblib` | Feature column order |
+
+## Usage
+
+### Load the model
+hon
+import joblib
+from huggingface_hub import hf_hub_download
+
+model = joblib.load(hf_hub_download(repo_id="bryium/house-price-predictor", filename="model.joblib"))
+preprocessor = joblib.load(hf_hub_download(repo_id="bryium/house-price-predictor", filename="preprocessor.joblib"))
+encodings = joblib.load(hf_hub_download(repo_id="bryium/house-price-predictor", filename="encodings.joblib"))
+feature_columns = joblib.load(hf_hub_download(repo_id="bryium/house-price-predictor", filename="feature_columns.joblib"))