Spam messages have increased dramatically in recent years even as the number of email clients has grown. Email has already become a valuable way of communicating because it saves time and effort. However, numerous emails contain unwelcome content known as spam as a result of social platforms and advertisements. Despite the fact that many techniques have already been created for spam mails categorization, none of them achieves 100 percent efficiency in analyzing spam messages. So, in this research, we propose a novel Gradient Fuzzy Guideline-based Spam Classifier (GFGSC) for classifying the spam e-mails as spam or non-spam. This research uses four types of datasets and these datasets are pre-processed using normalization. Then the set of data can be extracted using Principal Component Analysis (PCA) and Latent Semantic Analysis (LSA) techniques. The aspects are selected using Information Gain (IG) and Chi-Square (ChS) techniques. And the GFGSC classifier can be used for classifying the data as spam or non-spam with better effectiveness. Finally, the performances are examined and these metrics are matched with the existing approaches. The results are obtained using the MATLAB tool.

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