This guide provides a practical approach to Support Vector Classification (SVC), aimed at beginners who often struggle with achieving satisfactory results due to missing key steps. The authors propose a "cookbook" method that typically yields reasonable outcomes. The guide is not intended for advanced researchers but rather for novices to quickly and easily obtain acceptable results. The guide emphasizes the importance of data preprocessing, including converting categorical features into numeric data and scaling attributes to improve numerical stability. It recommends using the radial basis function (RBF) kernel as a first choice due to its ability to handle nonlinear relationships and fewer hyperparameters compared to other kernels. The guide also discusses model selection, particularly through cross-validation and grid-search, to find the optimal parameters $C$ and $\gamma$ for the RBF kernel. The guide includes real-world examples from users who initially struggled with accuracy. It provides detailed steps for data preprocessing, model selection, and parameter tuning. For large datasets, it suggests using LIBLINEAR, which is more efficient for linear classification tasks. The guide concludes with a discussion on when to use the linear kernel instead of the RBF kernel, particularly in scenarios where the number of features is much larger than the number of instances. It provides examples and comparisons to illustrate the effectiveness of different kernels in various contexts. The authors acknowledge the contributions of users of their SVM software LIBSVM and BSVM, who helped identify common difficulties for beginners. The guide is supported by detailed examples and references to relevant literature.This guide provides a practical approach to Support Vector Classification (SVC), aimed at beginners who often struggle with achieving satisfactory results due to missing key steps. The authors propose a "cookbook" method that typically yields reasonable outcomes. The guide is not intended for advanced researchers but rather for novices to quickly and easily obtain acceptable results. The guide emphasizes the importance of data preprocessing, including converting categorical features into numeric data and scaling attributes to improve numerical stability. It recommends using the radial basis function (RBF) kernel as a first choice due to its ability to handle nonlinear relationships and fewer hyperparameters compared to other kernels. The guide also discusses model selection, particularly through cross-validation and grid-search, to find the optimal parameters $C$ and $\gamma$ for the RBF kernel. The guide includes real-world examples from users who initially struggled with accuracy. It provides detailed steps for data preprocessing, model selection, and parameter tuning. For large datasets, it suggests using LIBLINEAR, which is more efficient for linear classification tasks. The guide concludes with a discussion on when to use the linear kernel instead of the RBF kernel, particularly in scenarios where the number of features is much larger than the number of instances. It provides examples and comparisons to illustrate the effectiveness of different kernels in various contexts. The authors acknowledge the contributions of users of their SVM software LIBSVM and BSVM, who helped identify common difficulties for beginners. The guide is supported by detailed examples and references to relevant literature.