Breast Cancer Diagnosis with Support Vector Machine

This project uses a Support Vector Machine (SVM) to classify breast tumors as malignant or benign based on various features. The dataset used in this project is provided in the "Cancer_Data.csv" file.

Prerequisites

Before you begin, ensure you have met the following requirements:

• Python (version 3.7 or higher)
• NumPy
• Matplotlib
• Pandas
• scikit-learn

You can install these dependencies using pip:

pip install numpy
pip install matplotlib
pip install pandas
pip install scikit-learn

Installation

1. Clone the repository to your local machine:

git clone https://github.com/Prometheussx/Breast-Cancer-SVM.git

2. Navigate to the project directory:

cd YourProject

Usage

1. Run the Jupyter Notebook or Python script to execute the breast cancer diagnosis using SVM.

python breast_cancer_diagnosis.py

2. View the results, including the accuracy of the SVM algorithm on the test data.

Result

Contact Information

For any questions, feedback or requests to contribute to the project, you can contact the contact information below:

• LinkedIn: [https://www.linkedin.com/in/erdem-taha-sokullu/]
• Email: [erdemtahasokullu@gmail.com]
• Kaggle: [https://www.kaggle.com/erdemtaha]

Data

The dataset used in this project, "Cancer_Data.csv," contains information about breast tumors. It includes various features and a target variable indicating whether the tumor is malignant (M) or benign (B).

• Data Link: Kaggle Link

Contributing

If you'd like to contribute to this project, please fork the repository and create a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.