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. 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
• Libraries: Scikit-learn, Pandas, Jupyter Notebook