A Machine Learning and NLP-Based Approach for Efficient Email Spam Detection Using TF-IDF and Logistic Regression | IJCT Volume 13 – Issue 3 | IJCT-V13I3P22
A Machine Learning and NLP-Based Approach for Efficient Email Spam Detection Using TF-IDF and Logistic Regression | IJCT Volume 13 – Issue 3 | IJCT-V13I3P22
In the digital era, the rapid growth of spam emails poses significant risks, including fraud and phishing attacks. This study presents a machine learning-based spam email detection system that classifies emails as spam or non-spam. The system employs Natural Language Processing (NLP) techniques such as tokenization, stopword removal, and TF-IDF vectorization to preprocess and transform textual data. A Logistic Regression model is trained on labeled datasets to identify patterns associated with spam messages. Additionally, a user-friendly interface is developed using Streamlit for real-time classification. The proposed system achieves high accuracy and demonstrates an effective approach to enhancing email security, with potential for further improvement using advanced models and larger datasets.
The Spam Email Detection System developed using machine learning successfully demonstrates the ability to classify emails as Spam or Not Spam with good accuracy. By utilizing techniques such as TF-IDF vectorization and the Naive Bayes algorithm, the system effectively analyzes textual data and identifies patterns commonly associated with spam messages.
The integration of the model with a Streamlit web application provides a simple and interactive interface, enabling users to input email content and receive instant predictions. The system is lightweight, fast, and efficient, making it suitable for real-time applications.
Overall, this project highlights how machine learning can be applied to enhance email security, reduce unwanted messages, and improve user experience.
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How to Cite This Paper
Amisha Kumari, Smriti Kumari, Pushpa Kumari, Sanjyoti Kumari, Kumar Amrendra (2026). A Machine Learning and NLP-Based Approach for Efficient Email Spam Detection Using TF-IDF and Logistic Regression. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
