Add beginner-friendly SVM exercise using Iris dataset

SVM_Exercise.md

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+# Support Vector Machine (SVM) Exercise
+
+**Objective:**  
+Learn how Support Vector Machines (SVMs) classify data by finding the best separating boundary (hyperplane) between classes.
+
+---
+
+## Step 1: Introduction
+
+Support Vector Machines are supervised learning models used for classification and regression tasks.  
+They work by finding the hyperplane that best separates different classes in a dataset.  
+SVMs can handle both **linear** and **non-linear** data using kernel functions.
+
+---
+
+## Step 2: Dataset Preparation
+
+Let’s use the famous **Iris dataset** from scikit-learn to visualize how SVMs work.
+
+```python
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+
+# Load dataset
+iris = datasets.load_iris()
+X = iris.data
+y = iris.target
+
+# Split dataset
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Standardize features
+scaler = StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+
+```
+
+## Step 3: Train an SVM Model
+
+```python
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score, classification_report
+
+# Create SVM classifier
+model = SVC(kernel='linear', C=1)
+
+# Train the model
+model.fit(X_train, y_train)
+
+# Make predictions
+y_pred = model.predict(X_test)
+
+# Evaluate
+print("Accuracy:", accuracy_score(y_test, y_pred))
+print("\nClassification Report:\n", classification_report(y_test, y_pred))
+```
+
+## Step 4: Visualize the Decision Boundary
+
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+
+def plot_decision_boundary(X, y, model):
+    X = X[:, :2]  # use only two features for easy visualization
+    h = .02
+    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
+    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
+    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
+                         np.arange(y_min, y_max, h))
+    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
+    Z = Z.reshape(xx.shape)
+    plt.contourf(xx, yy, Z, alpha=0.8)
+    plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', marker='o')
+    plt.title("SVM Decision Boundary (Linear Kernel)")
+    plt.xlabel("Feature 1")
+    plt.ylabel("Feature 2")
+    plt.show()
+
+plot_decision_boundary(X_train, y_train, model)
+```
+
+## Step 5: Try Different Kernels
+
+```python
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score
+
+kernels = ['linear', 'poly', 'rbf', 'sigmoid']
+
+for kernel in kernels:
+    model = SVC(kernel=kernel, C=1)
+    model.fit(X_train, y_train)
+    y_pred = model.predict(X_test)
+    print(f"\nKernel: {kernel}")
+    print("Accuracy:", accuracy_score(y_test, y_pred))
+```
+
+
+## Step 6: Summary
+
+In this exercise, you learned how to:
+
+- Load and preprocess data (Step 2)
+- Train an SVM classifier (Step 3)
+- Visualize decision boundaries (Step 4)
+- Experiment with different kernels (Step 5)
+
+This helps understand how SVMs find the optimal boundary between classes — a key concept in machine learning.
+
+
+**Tags:** #MachineLearning #SVM #Python #Hacktoberfest #BeginnerFriendly
+