This repository provides a cancer classification model using Support Vector Classifier (SVC). The model aims to classify cancer cases into benign or malignant based on various features obtained from medical examinations.

The dataset used in this project consists of medical data from cancer patients, including features such as tumor size, age, and other relevant factors. The dataset includes the following columns:

• Feature 1: Description of feature 1.
• Feature 2: Description of feature 2.
• ...
• Feature N: Description of feature N.
• Target: Binary target variable indicating benign (0) or malignant (1).

The dataset is available in the file cell_samples.csv.

Support Vector Classifier (SVC) is a machine learning algorithm used for classification tasks. It constructs a hyperplane or set of hyperplanes in a high-dimensional space to separate different classes. SVC aims to maximize the margin between the hyperplane and the closest data points of each class.

In this repository, we have implemented the SVC model using Python and the scikit-learn library. The code for training and evaluating the model can be found in the Cancer Classification.ipynb Jupyter Notebook.

To get started with the cancer classification model, follow these steps:

1. Clone this repository to your local machine.
2. Install the required dependencies.
3. Ensure you have Jupyter Notebook installed. If not, install it using pip install jupyter.
4. Open the Cancer Classification.ipynb Notebook using Jupyter Notebook.
5. Follow the instructions in the Notebook to load, preprocess, train, and evaluate the SVC model.
6. Once trained, you can use the model to predict the cancer class for new patient data.

After training the SVC model on the cancer dataset, you will be able to evaluate its performance and make predictions. The Notebook provides metrics such as accuracy, precision, recall, and F1 score to assess the model's effectiveness in classifying cancer cases.

Contributions to this repository are welcome. If you have any suggestions, bug fixes, or enhancements, please submit a pull request. We appreciate your contributions!

This project is licensed under the MIT License. See the LICENSE file for more details.

We would like to acknowledge the creators of the cancer dataset used in this project for providing valuable data for analysis and classification.

For any questions or inquiries, please contact Mohamed Shaad at [email protected].