Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/kasraskari/cancer-cell-class
cancer cell class prediction
https://github.com/kasraskari/cancer-cell-class
cancer-cells jupyter-notebook machine-learning python support-vector-machine svm
Last synced: 25 days ago
JSON representation
cancer cell class prediction
- Host: GitHub
- URL: https://github.com/kasraskari/cancer-cell-class
- Owner: KasrAskari
- Created: 2024-06-12T13:09:54.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-12-03T08:21:28.000Z (about 2 months ago)
- Last Synced: 2024-12-03T09:28:36.742Z (about 2 months ago)
- Topics: cancer-cells, jupyter-notebook, machine-learning, python, support-vector-machine, svm
- Language: Jupyter Notebook
- Homepage:
- Size: 137 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Cancer Cell Class Prediction
## Overview
This project implements a **Support Vector Machine (SVM)** model to classify cancer cells based on a dataset of cell samples. The goal is to provide an accurate classification system to distinguish between malignant and benign cells, achieving a **99% accuracy rate**.
## Features
- **High Accuracy:** The model achieves a high classification accuracy of 99%.
- **Machine Learning Algorithm:** Uses the SVM algorithm for robust classification.
- **Interactive Notebook:** The project is implemented in a Jupyter Notebook for easy experimentation and visualization.
- **Public Dataset:** Utilizes a publicly available dataset for training and evaluation.
## Dataset
The dataset used in this project is available on [Kaggle](https://www.kaggle.com/datasets/sam1o1/cell-samplescsv). It contains labeled samples of cell data, which are used to train and test the model.
## Project Structure
```
Cancer-Cell-Class/
│
├── Cancer_Cell.ipynb # Main Jupyter Notebook containing the code
├── cell_samples.csv # Dataset file
└── README.md # Project documentation
```
## Technologies Used
- **Python**: Programming language
- **Jupyter Notebook**: Interactive coding environment
- **SVM (Support Vector Machine)**: Machine learning algorithm
- **Pandas**: For data manipulation
- **Scikit-learn**: For machine learning model implementation
## Results
The SVM model achieves:
- **Accuracy:** 99%
- **Robust Classification** between malignant and benign cancer cells.
## License
This project is open-source and available under the MIT License.
## Acknowledgments
- Dataset: [Kaggle - Cell Samples Dataset](https://www.kaggle.com/datasets/sam1o1/cell-samplescsv)
- Libraries: Scikit-learn, Pandas, Jupyter Notebook