README.md

metadata

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:

   git clone <repository-url>
   cd Hackathon
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the application:

   streamlit run src/app.py
   

Docker Support

Build and run the container:

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)

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Developed for HEC Hackathon