Update app.py

app.py

@@ -187,9 +187,44 @@ def knn():
 def svm():
     svm = '''
     ### ⚡ Support Vector Machines (SVM)
-    SVM is a powerful classifier that works well for high-dimensional data. It tries to find the hyperplane that best separates the data points of different
+    SVM is a powerful classifier that works well for high-dimensional data. It tries to find the hyperplane that best separates the data points of different classes.
+
+    **📚 Example**:
+    - **🌸 Classifying Iris Flowers**: An SVM can be used to classify Iris flowers into different species.
+    ```python
+    from sklearn.svm import SVC
+    from sklearn.datasets import load_iris
+    data = load_iris()
+    X = data.data
+    y = data.target
+    model = SVC(kernel='linear')
+    model.fit(X, y)
+    predictions = model.predict(X)
+    ```
     '''
     return svm
+
+# Neural Networks
+def neural_networks():
+    neural = '''
+    ### 🧠 Neural Networks
+    Neural networks are modeled after the human brain, with layers of interconnected nodes (neurons) used for tasks like image and speech recognition.
+
+    **📚 Example**:
+    - **1️⃣2️⃣3️⃣ Classifying Handwritten Digits**: A simple neural network can be used to classify digits from the MNIST dataset.
+    ```python
+    from sklearn.neural_network import MLPClassifier
+    from sklearn.datasets import load_iris
+    data = load_iris()
+    X = data.data
+    y = data.target
+    model = MLPClassifier(hidden_layer_sizes=(10,), max_iter=1000)
+    model.fit(X, y)
+    predictions = model.predict(X)
+    ```
+    '''
+    return neural
+
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 st.sidebar.header("📚 Contents")