https://github.com/dimza5r/experiments-test
Minimal ML workflow skeleton for isolating core logic in notebooks. Validate mechanics without external dependencies. Explore the project on GitHub! 🚀📊
https://github.com/dimza5r/experiments-test
cybersecurity cypress cypress-io engagement enumeration exploit kubernetes-cluster kubernetes-deployment kubernetes-setup metasploit meterpreter object-detection php privilege-escalation redteam rust vulnerability wi-fi
Last synced: 3 months ago
JSON representation
Minimal ML workflow skeleton for isolating core logic in notebooks. Validate mechanics without external dependencies. Explore the project on GitHub! 🚀📊
- Host: GitHub
- URL: https://github.com/dimza5r/experiments-test
- Owner: dimza5r
- Created: 2025-06-23T17:29:51.000Z (4 months ago)
- Default Branch: develop
- Last Pushed: 2025-06-23T22:14:01.000Z (3 months ago)
- Last Synced: 2025-06-23T23:25:21.808Z (3 months ago)
- Topics: cybersecurity, cypress, cypress-io, engagement, enumeration, exploit, kubernetes-cluster, kubernetes-deployment, kubernetes-setup, metasploit, meterpreter, object-detection, php, privilege-escalation, redteam, rust, vulnerability, wi-fi
- Language: Jupyter Notebook
- Homepage: https://dimza5r.github.io
- Size: 15.3 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Experiments Test: A Minimal Sandbox for Machine Learning Workflows
 [](https://github.com/dimza5r/experiments-test/releases)
---
## Table of Contents
- [Overview](#overview)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Notebooks](#notebooks)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)
---
## Overview
The **Experiments Test** repository provides a minimal sandbox for isolating and testing core machine learning workflow logic. This setup is ideal for those looking to rebuild clean and reproducible foundations for future MLOps development. The project focuses on Jupyter notebooks, making it easy to experiment with various machine learning techniques.
---
## Features
- **Isolated Environment**: Each notebook runs in its own environment, ensuring that experiments do not interfere with one another.
- **Reproducibility**: The setup promotes reproducible research, allowing you to rerun experiments with consistent results.
- **Core Workflows**: Test and validate core machine learning workflows, from data preprocessing to model training.
- **Minimal Setup**: Quickly set up a clean environment with all necessary dependencies.
- **Traffic Vision**: Specifically designed for applications in traffic camera analysis and computer vision tasks.
---
## Installation
To get started, clone the repository and set up your environment:
```bash
git clone https://github.com/dimza5r/experiments-test.git
cd experiments-test
```
### Setting Up the Conda Environment
This project uses Conda for managing dependencies. Create a new environment with the required packages:
```bash
conda env create -f environment.yml
conda activate experiments-test
```
### Required Packages
The `environment.yml` file includes essential packages for machine learning and Jupyter notebook development:
- Python
- Jupyter
- NumPy
- Pandas
- Scikit-learn
- OpenCV
- Matplotlib
---
## Usage
To start using the notebooks, launch Jupyter Notebook from the terminal:
```bash
jupyter notebook
```
This command will open a web interface in your browser. Navigate to the notebooks folder to access the various experiments.
### Running Notebooks
Open a notebook and run the cells sequentially. Each notebook contains explanations and code snippets for various machine learning tasks.
---
## Notebooks
The repository contains several Jupyter notebooks, each focusing on different aspects of machine learning workflows:
1. **Data Preprocessing**: Clean and prepare data for analysis.
2. **Model Training**: Train various machine learning models and evaluate their performance.
3. **Traffic Camera Analysis**: Analyze traffic camera feeds for object detection and tracking.
4. **Computer Vision Tasks**: Explore image processing techniques using OpenCV.
You can find the notebooks in the `notebooks` directory.
---
## Contributing
We welcome contributions from the community. To contribute, follow these steps:
1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them with clear messages.
4. Push your branch and submit a pull request.
### Code of Conduct
Please adhere to the project's code of conduct to maintain a welcoming environment for all contributors.
---
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
---
## Contact
For questions or feedback, please reach out to the project maintainer:
- **Name**: Dimza5r
- **Email**: dimza5r@example.com
You can also check the [Releases](https://github.com/dimza5r/experiments-test/releases) section for the latest updates and download options.
---
Feel free to explore the project and contribute to its development. Your input is valuable in making this a robust resource for machine learning workflows!