Update README.md

README.md

@@ -1,93 +1,38 @@
 ---
-language: en
-datasets:
-  - house-prices-advanced-regression-techniques
+license: apache-2.0
 tags:
   - regression
-  - linear-regression
-  - scikit-learn
-  - tabular-data
   - house-price-prediction
-  - india
+  - tabular
+  - sklearn
+  - hyderabad
+  - real-estate
+datasets:
+  - your-username/your-dataset-name
 metrics:
   - r2
   - rmse
-license: mit
 model-index:
-  - name: House Price Prediction India - Linear Regression
+  - name: Hyderabad House Price Predictor
     results:
       - task:
-          type: regression
-          name: Tabular Regression
+          type: tabular-regression
+          name: House Price Prediction
         dataset:
-          name: House Prices (Kaggle)
-          type: house-prices-advanced-regression-techniques
+          name: Hyderabad House Price Dataset
+          type: your-username/your-dataset-name
         metrics:
-          - type: r2
-            value: 0.87
-          - type: rmse
-            value: 32000
----
-
-# 🏡 House Price Prediction Model (India)
-
-A regression model trained on Kaggle's House Prices dataset to predict sale prices of residential homes in India based on features like square footage, location, and number of bedrooms.
-
+          - name: R² Score
+            type: r2
+            value: 0.85  # Replace with your actual result
+          - name: Root Mean Squared Error (RMSE)
+            type: rmse
+            value: 3.42  # Replace with your actual result
 ---
 
-## 📌 Model Details
+# 🏠 Hyderabad House Price Prediction Model
 
-| Detail         | Description          |
-|----------------|----------------------|
-| **Model type** | Linear Regression    |
-| **Framework**  | Scikit-learn         |
-| **Language**   | Python               |
-| **Task**       | Regression           |
-| **License**    | MIT or Apache-2.0    |
+This model predicts housing prices in **Hyderabad, India** based on features such as area, location, number of bedrooms. It was trained on a custom dataset containing over 3,600 features extracted and engineered from a real estate dataset.
 
 ---
 
-## 📊 Intended Use
-
-This model is built for **educational and experimental use**. It demonstrates the use of basic machine learning techniques like:
-
-- Linear Regression
-- Feature Engineering
-- Data Cleaning
-- Model Evaluation (RMSE, R²)
-
----
-
-## 📂 Dataset
-
-- **Source**: [Kaggle House Prices – Advanced Regression Techniques](https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques)
-- **Features Used** (example subset):
-  - `GrLivArea` – Above ground living area
-  - `OverallQual` – Overall material and finish quality
-  - `YearBuilt` – Original construction year
-  - `GarageCars` – Number of cars in garage
-  - `TotalBsmtSF` – Total basement area
-
----
-
-## ⚙️ How to Use
-
-```python
-import joblib
-import pandas as pd
-
-# Load trained model
-model = joblib.load("house_price_model.pkl")
-
-# Create input features
-input_data = pd.DataFrame({
-    "GrLivArea": [1500],
-    "OverallQual": [7],
-    "YearBuilt": [2005],
-    "GarageCars": [2],
-    "TotalBsmtSF": [800]
-})
-
-# Make prediction
-predicted_price = model.predict(input_data)
-print(predicted_price)