Author(s) :

Akshar Grover, Kanishka Chaturvedi, Grishi Sachdeva.

Volume/Issue :

Abstract :

Spam emails pose a significant challenge by inundating inboxes with unsolicited messages, advertisements, and potential security threats such as phishing and malware. This study explores the application of Natural Language Processing (NLP) and machine learning techniques for spam detection. Using a structured dataset, we preprocess and extract features from email content before applying classification algorithms. The study evaluates two models: Logistic Regression and a deep neural network with a multi-layered architecture. Results indicate that the neural network outperforms Logistic Regression in terms of accuracy and adaptability. This research contributes to enhancing spam detection methodologies by improving classification accuracy and minimizing false positives.

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