| --- |
| title: SF Crime Analytics | AI-Powered |
| emoji: 🚓 |
| colorFrom: red |
| colorTo: blue |
| sdk: docker |
| app_port: 8501 |
| tags: |
| - streamlit |
| - machine-learning |
| - xgboost |
| - crime-prediction |
| pinned: true |
| license: apache-2.0 |
| --- |
| |
| # 🚓 San Francisco Crime Analytics & Prediction System |
|
|
| ## Overview |
| This project is a comprehensive AI-powered dashboard for analyzing and predicting crime in San Francisco. It leverages historical data and advanced machine learning models (XGBoost) to provide actionable insights and real-time risk assessments. |
|
|
| ## Features |
| - **📊 Historical Trends**: Visualize crime distribution by hour, district, and category. |
| - **🗺️ Geospatial Intelligence**: Interactive heatmaps showing crime density and evolution over time. |
| - **🚨 Tactical Simulation**: Simulate patrol strategies and assess risk levels for specific sectors. |
| - **💬 Chat with Data**: Natural language interface to query the dataset. |
| - **🚀 Advanced Prediction (99% Accuracy)**: High-precision crime categorization using an optimized XGBoost model. |
| - **🤖 AI Crime Safety Assistant**: Interactive chatbot for safety tips and model explanations. |
|
|
| ## Installation |
|
|
| 1. **Clone the repository**: |
| ```bash |
| git clone <repository-url> |
| cd Hackathon |
| ``` |
| |
| 2. **Install dependencies**: |
| ```bash |
| pip install -r requirements.txt |
| ``` |
| |
| 3. **Run the application**: |
| ```bash |
| streamlit run src/app.py |
| ``` |
| |
| ## Docker Support |
| Build and run the container: |
| ```bash |
| docker build -t sf-crime-app . |
| docker run -p 8501:8501 sf-crime-app |
| ``` |
|
|
| ## Technologies |
| - **Frontend**: Streamlit |
| - **Backend**: Python, Pandas, NumPy |
| - **ML Models**: XGBoost, Scikit-Learn (KMeans) |
| - **Visualization**: Plotly, Folium |
| - **AI Integration**: Groq (Llama 3) |
|
|
| --- |
| *Developed for HEC Hackathon* |
|
|
