Support Vector Classification (SVC) Implementation

This project demonstrates the implementation of Support Vector Classification (SVC) using Python and Scikit-learn.
It explores how SVMs can be used for supervised learning classification tasks, including model training, testing, and evaluation.

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🚀 Features

• Data preprocessing and splitting into train/test sets
• Implementation of Support Vector Classifier (SVC) using Scikit-learn
• Model training on labeled data
• Evaluation using metrics such as:
  • Confusion Matrix
  • Accuracy Score
  • Classification Report
• Visualization of decision boundaries (if applicable)

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🛠️ Technologies Used

• Python 3.x
• Jupyter Notebook
• NumPy
• Pandas
• Matplotlib / Seaborn
• Scikit-learn

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📂 Project Structure

├── svc_implementation.ipynb # Jupyter Notebook with full implementation ├── README.md # Project documentation └── data/ # (Optional) Dataset used for training/testing

yaml Copy code

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⚡ How to Run

1. Clone the repository:

   git clone https://github.com/your-username/svc-implementation.git
   cd svc-implementation

Install required dependencies:

bash Copy code pip install -r requirements.txt Open Jupyter Notebook:

bash Copy code jupyter notebook svc_implementation.ipynb 📊 Results The trained SVC model provides classification performance with metrics like accuracy and precision.

Example Confusion Matrix and Accuracy are included in the notebook.

🔮 Future Improvements Hyperparameter tuning with GridSearchCV Testing with different kernels (linear, polynomial, RBF, sigmoid) Adding cross-validation for better performance estimation Comparing SVC with other classifiers (Logistic Regression, Random Forest, etc.)