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