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Event Crow Model Detection

This project is an AI-powered pipeline for predicting and simulating crowd behaviors at large-scale events (concerts, football matches, festivals, parades, etc.). The goal is to help event organizers and safety teams make data-driven decisions for smooth entry/exit, congestion management, and emergency planning.

Project Structure

event-crowd-model/
├── .gitignore                   # Ignore unnecessary files (e.g., logs, temp, venv, data dumps)
├── data/
│   ├── raw/                     # Raw datasets (ticketing, transport schedules, sensors, etc.)
│   ├── clean/                   # Processed datasets after cleaning & feature engineering
│   ├── simulations/             # Synthetic/simulated datasets for crowd flow scenarios
├── deployment/
│   ├── app.py                   # Deployment entrypoint (e.g., FastAPI/Flask app for inference)
│   ├── deploy.sh                # Deployment automation script (build, push, run)
│   ├── DockerFile               # Docker container definition for reproducibility
├── main.ipynb                   # Jupyter notebook for exploration, EDA, prototyping & pipeline demo
├── models/
│   ├── congestion/              # Models handling congestion prediction (e.g., bottlenecks, density)
│   ├── forecasting/             # Time-series forecasting models (e.g., arrivals, flow rates)
│   ├── rl/                      # Reinforcement learning agents for crowd management strategies
├── README.md                    # Project overview, setup instructions, usage guide
├── requirements.txt             # Python dependencies for environment setup
├── src/
│   ├── model_evaluation/        # Evaluation scripts (metrics, visualization, benchmarking)
│   │   ├── model_evaluation.py  # Functions to assess model performance
│   ├── model_training/          
│   │   ├── classification.py    # Classification models (e.g., crowd state categories)
│   │   ├── reinforcement.py     # RL training pipeline (agents, reward design, env interface)
│   │   ├── time_series.py       # Time-series models (forecasting arrivals/flows)
│   ├── preprocessing/
│   │   ├── data_cleaning.py     # Data cleaning, handling missing values, outlier detection
│   ├── utils/
│   │   ├── helper_functions.py  # Utility functions (logging, configs, data loaders, etc.)


Setup Instructions

1. Clone the repo

git clone "https://github.com/GreatAI-NeuBee/event-crowd-model.git"
cd event-crowd-model

2. Create Virtual Environment

python -m venv eventcrowd_env
eventcrowd_env\Scripts\activate 

3. Install dependencies

pip install -r requirements.txt

4. Run jupyter notebook

  • Open main.ipynb and explore pipeline

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