Machine learning (ML) is a powerful tool for automated phishing email detection, but existing techniques like support vector machines and Naive Bayes have proven slow or ineffective in handling spam filtering. This study attempts to provide a phishing email detector and reliable cl...
Human and LLM dataset from Kaggle (link) Fraud dataset from Kaggle (link). All emails are assumed to be human written, as they were collected before the rise of LLMs Once training was complete, our model was shown one email after the other and asked to determine whether that example is ...
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Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Learn more OK, Got it.Phanendra +1· 1y ago· 317 views arrow_drop_up3 Copy & Edit13 more_vert spam email detectionNotebookInputOutputLogsComments (2)Logs...
Explore and run machine learning code with Kaggle Notebooks | Using data from Phishing Email Detection
menu Create stpete_ishii·6mo ago· 82 views arrow_drop_up2 Copy & Edit3 more_vert Copied from stpete_ishii (+10,-7) historyVersion 5 of 5chevron_right Runtime play_arrow 1m 30s Language Python Table of Contents Email Spam Detection CountVectorizer XGBdef spacy_tokenizer(sentence):CountVec...
Explore and run machine learning code with Kaggle Notebooks | Using data from The Enron Email Dataset📩📪
Explore and run machine learning code with Kaggle Notebooks | Using data from SMS Spam Collection Dataset
Harsha Kadam and Paniskaki [28] introduce a multi-label classification for emails (without spam detection) using techniques like SVM, GRU (Gated Recurrent Unit), CNN (Convolutional Neural Network), and Transformer [29]; nevertheless, we find their methodology improper. Their labels were predefined...