https://github.com/jpcadena/cancer-classification
Breast cancer classification project.
https://github.com/jpcadena/cancer-classification
cancer-detection classification data-analysis data-science deep-learning imblearn machine-learning neuronal-network numpy pandas pylint python scikit-learn supervised-learning tensorflow
Last synced: about 1 month ago
JSON representation
Breast cancer classification project.
- Host: GitHub
- URL: https://github.com/jpcadena/cancer-classification
- Owner: jpcadena
- License: mit
- Created: 2023-03-09T17:53:37.000Z (almost 3 years ago)
- Default Branch: master
- Last Pushed: 2023-03-20T19:00:06.000Z (almost 3 years ago)
- Last Synced: 2025-03-05T06:23:04.902Z (11 months ago)
- Topics: cancer-detection, classification, data-analysis, data-science, deep-learning, imblearn, machine-learning, neuronal-network, numpy, pandas, pylint, python, scikit-learn, supervised-learning, tensorflow
- Language: Python
- Homepage: https://github.com/jpcadena/cancer-classification
- Size: 625 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# cancer-classification
Table of Contents
## About the project
![Project][project-screenshot]
This project is about Cancer classification and uses multiple Machine
Learning Models to test their accuracy to classify the patient with a
malign or a benign tumor. The project structure includes multiple steps in
a Machine Learning Operations project including packages for analysis,
engineering, modelling, reports, etc.
### Built with
* [![Python][Python.org]][Python-url]
## Getting started
### Prerequisites
* [Python 3.10+][Python-docs-url]
### Installation
1. Clone the **repository**
```
git clone https://github.com/jpcadena/cancer-classification.git
```
2. Change the directory to **root project**
```
cd cancer-classification
```
3. Create a **virtual environment** *venv*
```
python3 -m venv venv
```
4. Activate **environment** in Windows
```
.\venv\Scripts\activate
```
5. Or with Unix/Mac OS X
```
source venv/bin/activate
```
6. Install requirements with PIP
```
pip install -r requirements.txt
```
### Usage
1. If found **sample.env**, rename it to **.env**.
2. Replace your **credentials** into the *.env* file.
3. Execute with console
```
python main.py
```
## Contributing
If you have a suggestion that would make this better, please fork the repo and
create a pull request.
1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
Use docstrings with **reStructuredText** format by adding triple double quotes
**"""** after function definition.\
Add a brief function description, also for the parameters including the return
value and its corresponding data type.\
Please use **linting** to check your code quality
following [PEP 8](https://peps.python.org/pep-0008/).\
Check documentation
for [Visual Studio Code](https://code.visualstudio.com/docs/python/linting#_run-linting)
or [Jetbrains Pycharm](https://github.com/leinardi/pylint-pycharm/blob/master/README.md).\
Recommended plugin for
autocompletion: [Tabnine](https://www.tabnine.com/install)
## License
Distributed under the MIT License.
## Contact
LinkedIn: [Juan Pablo Cadena Aguilar][linkedin-url]
E-mail: [Juan Pablo Cadena Aguilar](mailto:jpcadena@espol.edu.ec?subject=[GitHub]cancer-classification)
[linkedin-url]: https://linkedin.com/in/juanpablocadenaaguilar
[project-screenshot]: assets/static/images/project.png
[Python.org]: https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54
[Python-url]: https://www.python.org/
[Python-docs-url]: https://docs.python.org/3.10/
