Add MLP activation function comparison script

Added a new script (mlp_activation_comparison.py) that demonstrates the effect of different activation functions 
('relu', 'tanh', 'logistic') on a simple dataset using scikit-learn's MLPClassifier. 
This helps visualize and understand how activation choices influence model performance.

Author: Karthikn-VR

machine_learning/mlp_activation_comparison.py

@@ -0,0 +1,23 @@
+import matplotlib.pyplot as plt
+from sklearn.neural_network import MLPClassifier
+from sklearn.datasets import make_moons
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+X, y = make_moons(n_samples=500, noise=0.2, random_state=42)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+
+activations = ["logistic", "tanh", "relu"]
+results = {}
+
+for act in activations:
+    clf = MLPClassifier(hidden_layer_sizes=(10, 5), activation=act, solver="adam", max_iter=1000, random_state=42)
+    clf.fit(X_train, y_train)
+    y_pred = clf.predict(X_test)
+    acc = accuracy_score(y_test, y_pred)
+    results[act] = acc
+
+plt.bar(results.keys(), results.values())
+plt.title("Activation Function Comparison")
+plt.ylabel("Accuracy")
+plt.show()