🔬 Cancer Cell Classification

📝 Overview

This project leverages a Support Vector Machine (SVM) model to classify cancer cells based on sample data. The objective is to distinguish between malignant and benign cells with high accuracy, achieving an impressive 99% classification accuracy.

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🚀 Features

✅ High Accuracy: The model achieves a 99% classification rate.
✅ Robust ML Algorithm: Utilizes SVM for reliable cancer cell classification.
✅ Interactive Notebook: Implemented in Jupyter Notebook for ease of experimentation.
✅ Real-World Dataset: Uses a publicly available dataset for training and evaluation.

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📊 Dataset

The dataset used in this project is sourced from Kaggle. It contains labeled samples of cell data, which are essential for training and testing the classification model.

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📂 Project Structure

Cancer-Cell-Class/  
│  
├── Cancer_Cell.ipynb   # Main Jupyter Notebook containing the code  
├── cell_samples.csv    # Dataset file  
└── README.md           # Project documentation 

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🛠 Technologies Used

• Python 🐍 – Core programming language
• Jupyter Notebook 📓 – Interactive coding environment
• SVM (Support Vector Machine) 🤖 – Machine learning algorithm
• Pandas 📊 – Data manipulation library
• Scikit-learn 🔍 – ML model implementation

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📈 Results

📌 Accuracy: 99%
📌 Reliable classification of malignant vs. benign cancer cells

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📜 License

This project is open-source and available under the MIT License.

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🙌 Acknowledgments

🔗 Dataset: Kaggle - Cell Samples Dataset
🔗 Libraries Used: Scikit-learn, Pandas, Jupyter Notebook