Detection of spam or ham

Keywords: Spam, Detection

Background

The project's focus is on utilizing deep learning techniques and natural language processing to detect "ham" data, or legitimate messages, within a dataset. By training a model to recognize linguistic patterns associated with non-spam content, the project aims to enhance email and text communication by accurately filtering out unwanted or irrelevant messages. This endeavor contributes to improving communication efficiency and user experience, safeguarding against the proliferation of spam content on various platforms.

Aim & Objectives

Aim
To Detect the Ham data using DL Techniques
Objective
Build spam detection using deep learning for accurate email filtering.
Develop a neural network to distinguish spam ("ham") messages effectively.
Implement efficient deep learning for reliable spam identification.

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

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