| # SF Crime Prediction App |
|
|
| This is a Streamlit application for predicting crime categories in San Francisco using an XGBoost model. |
|
|
| ## Setup |
|
|
| 1. **Install Dependencies**: |
| ```bash |
| pip install -r requirements.txt |
| ``` |
| |
| 2. **Run the App**: |
| ```bash |
| streamlit run streamlit_app.py |
| ``` |
| Or simply double-click `run_app.bat`. |
| |
| ## Model Info |
|
|
| The app uses `crime_xgb_artifacts.pkl` which contains: |
| - XGBoost Model |
| - LabelEncoder for Target (Crime Category) |
| - FeatureHashers for Address and Description |
|
|
| **Note**: The model expects specific features including hashed Address and Description. Ensure you provide these inputs in the UI for accurate predictions. |
| **Note**: The District encoder was missing from the provided files, so a default alphabetical mapping is used. |
|
|
| ## Deployment |
|
|
| To deploy on the web (e.g., Streamlit Cloud): |
| 1. Push this code to a GitHub repository. |
| 2. Sign up for [Streamlit Cloud](https://streamlit.io/cloud). |
| 3. Connect your GitHub and deploy the app. |
|
|
