SVM Prediction Example with Code Implementation

The following is a practical demonstration of using Support Vector Machine (SVM) for prediction tasks. SVM is a supervised machine learning algorithm widely used for both classification and regression analysis. The core mechanism involves finding the optimal hyperplane that maximizes the margin between different classes in the feature space, using kernel functions to handle non-linear separability. This example utilizes a banking customer dataset containing features such as age, annual income, credit score, and savings account status. The target variable indicates whether customers have obtained loans from the bank. During implementation, we employ scikit-learn's StandardScaler for feature normalization to ensure all variables contribute equally to the model. The code implementation involves splitting the dataset into training and testing sets using train_test_split, followed by initializing an SVM classifier with appropriate kernel selection (e.g., linear or RBF). Key parameters like C (regularization) and gamma (kernel coefficient) are tuned using GridSearchCV for optimal performance. The model training phase uses the fit() method on preprocessed training data. For performance evaluation, we calculate metrics including accuracy, precision, recall, and F1-score through sklearn's classification_report function. The confusion matrix visualization provides additional insights into classification patterns. This practical demonstration showcases how SVM algorithms can be effectively implemented for real-world predictive analytics in financial services.