Email is one of the most crucial forms of communication in daily life, be it academic or business. Yet with the growing volume of email, comes a growing amount of spam emails. Spam emails are not only annoying but they are also dangerous because it may have links for phishing and other malicious activities as well. The strategy for detection using filter such as spam email detection does not work the same way because perpetrators change their strategies to avoid filters. So, its need of the hour to develop intelligent and automated spam email detection systems which learn and adapt. In this research, we design and build a model for Spam Email Detection using the Machine Learning and Natural Language Processing techniques. This research aims to develop a model that can classify emails based on email content using spam and ham emails. Natural Language Processing is also used because email data is pre-processed. Pre-processing — Various methods used to clean and normalize raw email data These include tokenization, stop word removal, lower casing and lemmatization. After pre-processing, feature extraction techniques like Term Frequency-Inverse Document Frequency (TF-IDF) are used to convert the raw email data into a representation that machine learning algorithms can comprehend and work with. Various supervised machine learning models including Naïve Bayes, SVM, Logistic Regression and Random forest are implemented followed by performance comparison. The models are trained on labeled datasets and then the performance of the models is evaluated using various metrics like accuracy, precision, recall, F1 score. Through this experiment we can figure out that the classification of spam messages or accuracy is highly improvised in machine learning models w.r.t. old strategies. For instance, SVM and Logistic Regression models perform well with high precision and low false positive rates so that normal messages are not predicted to be spam. The findings of this study validate that with the help of defining higher-level features using NLP models, we can achieve improved accuracy by combining machine learning models to form a robust spam filter model, which could be scaled upwards for adapting to the changing characteristics of spam messages as well as providing useful performances in real-time. Future Directions The next step can combine deep learning models with powerful word embeddings that increase the performance of the model in spam message classification. In this paper we demonstrate the importance of intelligent methods for safe email communication models.
Spam Email detection , Machine Learning, Natural Language Pro Naïve Bayes, TF-IDF , Support Vector Machine, Text Classification , Cybersecurity, Email Filtering, Data Mining, Email Security, Data Preprocessing, Text Vectorization, Pattern Recognition, Modelling, Intelligent Spam Filtering.
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