Create README.md

README.md

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+---
+language: en
+datasets:
+  - house-prices-advanced-regression-techniques
+tags:
+  - regression
+  - linear-regression
+  - scikit-learn
+  - tabular-data
+  - house-price-prediction
+  - india
+metrics:
+  - r2
+  - rmse
+license: mit
+model-index:
+  - name: House Price Prediction India - Linear Regression
+    results:
+      - task:
+          type: regression
+          name: Tabular Regression
+        dataset:
+          name: House Prices (Kaggle)
+          type: house-prices-advanced-regression-techniques
+        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.
+
+---
+
+## 📌 Model Details
+
+| Detail         | Description          |
+|----------------|----------------------|
+| **Model type** | Linear Regression    |
+| **Framework**  | Scikit-learn         |
+| **Language**   | Python               |
+| **Task**       | Regression           |
+| **License**    | MIT or Apache-2.0    |
+
+---
+
+## 📊 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)