Added SVM classifier and regressor with Streamlit visualization

models/svm.py

@@ -0,0 +1,42 @@
+import numpy as np
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+from sklearn.svm import SVC, SVR
+from sklearn.metrics import accuracy_score, mean_squared_error
+
+class SVMClassifier:
+    def __init__(self, kernel='linear', C=1.0):
+        self.kernel = kernel
+        self.C = C
+        self.model = SVC(kernel=self.kernel, C=self.C)
+
+    def train(self, X, y):
+        self.model.fit(X, y)
+
+    def predict(self, X):
+        return self.model.predict(X)
+
+    def evaluate(self, X, y):
+        y_pred = self.predict(X)
+        return accuracy_score(y, y_pred)
+
+    def get_support_vectors(self):
+        return self.model.support_vectors_
+
+
+class SVMRegressor:
+    def __init__(self, kernel='rbf', C=1.0, epsilon=0.1):
+        self.kernel = kernel
+        self.C = C
+        self.epsilon = epsilon
+        self.model = SVR(kernel=self.kernel, C=self.C, epsilon=self.epsilon)
+
+    def train(self, X, y):
+        self.model.fit(X, y)
+
+    def predict(self, X):
+        return self.model.predict(X)
+
+    def evaluate(self, X, y):
+        y_pred = self.predict(X)
+        return mean_squared_error(y, y_pred, squared=False)  # RMSE

pages/svm.py

@@ -0,0 +1,62 @@
+import streamlit as st
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+from models.svm import SVMClassifier, SVMRegressor
+
+st.title("🔷 Support Vector Machine (SVM) Simulator")
+
+option = st.radio("Choose Mode", ["Classification", "Regression"])
+
+if option == "Classification":
+    st.subheader("SVM Classifier Visualization")
+
+    # Load toy dataset
+    X, y = datasets.make_blobs(n_samples=100, centers=2, random_state=6, cluster_std=1.2)
+
+    kernel = st.selectbox("Kernel", ["linear", "poly", "rbf", "sigmoid"])
+    C = st.slider("Regularization (C)", 0.01, 10.0, 1.0)
+
+    model = SVMClassifier(kernel=kernel, C=C)
+    model.train(X, y)
+
+    # Plot decision boundary
+    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.linspace(x_min, x_max, 300),
+                         np.linspace(y_min, y_max, 300))
+    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
+    Z = Z.reshape(xx.shape)
+
+    plt.figure(figsize=(8, 6))
+    plt.contourf(xx, yy, Z, cmap='coolwarm', alpha=0.6)
+    plt.scatter(X[:, 0], X[:, 1], c=y, cmap='coolwarm', edgecolors='k')
+    plt.scatter(model.get_support_vectors()[:, 0], model.get_support_vectors()[:, 1],
+                s=100, facecolors='none', edgecolors='yellow', label='Support Vectors')
+    plt.legend()
+    st.pyplot(plt)
+    st.write(f"**Accuracy:** {model.evaluate(X, y):.2f}")
+
+else:
+    st.subheader("SVM Regressor Visualization")
+
+    # Generate regression dataset
+    X = np.sort(5 * np.random.rand(100, 1), axis=0)
+    y = np.sin(X).ravel() + np.random.randn(100) * 0.1
+
+    kernel = st.selectbox("Kernel", ["linear", "poly", "rbf", "sigmoid"])
+    C = st.slider("Regularization (C)", 0.1, 10.0, 1.0)
+    epsilon = st.slider("Epsilon", 0.01, 1.0, 0.1)
+
+    model = SVMRegressor(kernel=kernel, C=C, epsilon=epsilon)
+    model.train(X, y)
+    y_pred = model.predict(X)
+
+    # Plot regression curve
+    plt.figure(figsize=(8, 6))
+    plt.scatter(X, y, color="blue", label="Data")
+    plt.plot(X, y_pred, color="red", label="SVM Prediction")
+    plt.title("SVM Regression")
+    plt.legend()
+    st.pyplot(plt)
+    st.write(f"**RMSE:** {model.evaluate(X, y):.3f}")