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---
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*
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