Task 7 – SVM Classification

🔍 Objective

Apply Support Vector Machines (SVM) using both linear and RBF kernels on a binary classification problem (Breast Cancer Dataset).

---

📚 Dataset

• Breast Cancer Wisconsin Dataset from Scikit-learn or Kaggle.

---

🛠 Tools & Libraries

• Python
• Scikit-learn
• Pandas
• Matplotlib
• Seaborn
• NumPy

---

✅ Steps Performed

1. Loaded and preprocessed dataset.
2. Scaled features using StandardScaler.
3. Trained SVM using:
   • Linear Kernel
   • RBF Kernel
4. Evaluated using Confusion Matrix and Classification Report.
5. Performed Hyperparameter Tuning using GridSearchCV.
6. Visualized decision boundary using 2D features.

---

📊 Results

• Achieved high accuracy on both training and testing data.
• Best hyperparameters obtained: C=1, gamma=0.1 with RBF kernel.

---

📷 Screenshots (if any)

---

🔗 Submission

This project is submitted as part of the Elevate AI/ML Internship – Task 7.