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Data Science: Support Vector Machines (SVM) & Neural Networks 🧠
In this video, I break down two powerful machine learning classification methods - Support Vector Machines and Neural Networks - and compare their performance on the same dataset.
What I Cover:
✓ Setting up the Python environment with scikit-learn
✓ Creating and splitting a classification dataset (Age, Salary → Purchase prediction)
✓ Implementing SVM (Support Vector Classifier) with linear kernel
✓ Building a Neural Network using MLPClassifier
✓ Evaluating models with confusion matrix, accuracy, precision, recall, and F1-score
✓ Why SVM performed better (100% accuracy) vs Neural Network (67% accuracy)
✓ Understanding when to use each algorithm
✓ What happens when you tune neural network parameters (spoiler: it got worse!)
Key Libraries Used:

pandas
scikit-learn (SVC, MLPClassifier, train_test_split)
Classification metrics (confusion_matrix, accuracy_score, classification_report)

Main Takeaway: SVM excels with small datasets and clear boundaries, while Neural Networks need lots of data to shine. Size matters in machine learning! 📈
Perfect for students learning machine learning fundamentals and practical Python implementation.
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