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https://github.com/ramironunes/traffic-accident-analysis
This repository contains an analysis of traffic accidents aimed at forecasting future incidents using the SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous variables) model. We predict traffic accidents by correlating historical data with traffic volume from toll stations.
https://github.com/ramironunes/traffic-accident-analysis
artificial-intelligence data-science docker docker-compose dockerfile orange python
Last synced: 29 days ago
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This repository contains an analysis of traffic accidents aimed at forecasting future incidents using the SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous variables) model. We predict traffic accidents by correlating historical data with traffic volume from toll stations.
- Host: GitHub
- URL: https://github.com/ramironunes/traffic-accident-analysis
- Owner: ramironunes
- License: mit
- Created: 2024-07-05T16:01:32.000Z (7 months ago)
- Default Branch: master
- Last Pushed: 2024-09-01T16:17:02.000Z (5 months ago)
- Last Synced: 2024-11-20T14:50:59.753Z (3 months ago)
- Topics: artificial-intelligence, data-science, docker, docker-compose, dockerfile, orange, python
- Language: Python
- Homepage:
- Size: 212 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# Traffic Accident Analysis
This repository aims to analyze traffic accident data using Python and various data science tools. The project includes a Docker setup to ensure a consistent development environment with all necessary dependencies.
## Technologies Used
- **Conda**: Used to manage dependencies and create isolated environments.
- **Docker**: Ensures a consistent and reproducible environment for development and deployment.
- **Orange3**: A comprehensive suite for machine learning and data mining.
- **Python**: The main programming language used for data analysis and processing.
## Getting Started
### Prerequisites
Ensure you have Docker and Docker Compose installed on your system.
### Installing Docker and Docker Compose
To install Docker and Docker Compose, follow these steps:
1. **Install Docker:**
```bash
sudo apt-get update
sudo apt-get install -y docker-ce docker-ce-cli containerd.io
```
2. **Start the Docker Container:**
```bash
docker compose -f docker/docker-compose.dev.yml up -d
```
3. **Allow X11 connections:**
Allow X11 connections:
```bash
xhost +local:docker
```
4. **Access the Container:**
To access the container in interactive mode, run:
```bash
docker compose -f docker/docker-compose.dev.yml exec traffic-analysis bash
```
5. **Initialize Conda and Activate the Environment:**
Inside the container, initialize Conda and activate the environment:
```bash
source /opt/conda/etc/profile.d/conda.sh
conda activate traffic_analysis
```
6. **Start Orange:**
Once the environment is activated, start Orange with:
```bash
orange-canvas
```
### Project Dependencies
Dependencies are managed via Conda and are specified in the deploy/requirements.txt file. Key dependencies include:
- **Orange3**: A comprehensive suite for machine learning and data mining.
### Project Scripts
- `setup.py`: Configuration for the Python package, including project metadata and dependencies.
### Docker Configuration
- `docker/Dockerfile.dev`: Defines the Docker image for the development environment, based on Miniconda.
- `docker/docker-compose.dev.yml`: Docker Compose configuration for setting up the development environment.