| title: California House Price Prediction | |
| emoji: 🏠 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 4.0.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| # California House Price Prediction 🏠 | |
| Interactive demo for predicting California house prices using a Random Forest model. | |
| ## How to Use | |
| 1. Adjust the sliders to set house features: | |
| - **Location**: Longitude and Latitude | |
| - **Age**: Housing median age | |
| - **Size**: Total rooms, bedrooms, population, households | |
| - **Income**: Median income in the area | |
| - **Proximity**: Distance to ocean | |
| 2. Click **Submit** to get the predicted house price | |
| ## Model | |
| This app uses the [house-price-prediction](https://huggingface.co/niru-nny/house-price-prediction) model trained on California Housing dataset. | |
| **Features:** | |
| - Random Forest Regressor (scikit-learn) | |
| - RMSE: ~$47,000-49,000 | |
| - Trained on 20,640 California districts | |
| ## Examples | |
| Try these sample inputs: | |
| - **Bay Area House**: High median income, near bay location | |
| - **Inland House**: Lower median income, inland location | |