SVM预测示例

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SVM预测示范例子

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The following is a demonstration example of using Support Vector Machine (SVM) for prediction. SVM is a machine learning algorithm used for classification and regression analysis. It works by finding the optimal hyperplane that separates and classifies data into different classes.

In this example, we will use a dataset that contains information about customers of a bank. The dataset includes features such as age, income, credit score, and whether or not the customer has a savings account. The target variable is whether or not the customer has taken out a loan from the bank.

After preprocessing the data by scaling and normalizing the features, we will fit the SVM model to the training data. We will then evaluate the model's performance on the testing data using metrics such as accuracy, precision, recall, and F1-score.

By demonstrating the use of SVM for prediction on a real-life dataset, we hope to provide a better understanding of how this machine learning algorithm can be used in practical applications.