Spam Alert System Application using AI, Machine Learning, and Deep Learning Techniques

## Project Request

The objective of this project is to develop a spam alert system application that can detect and filter spam in calls, emails, and SMS using AI, machine learning, and deep learning techniques.
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| Field | Description |
| ------ | --------------------------------- |
| About | Spam Alert System Application using AI, Machine Learning, and Deep Learning Techniques |
| Github | Ashgen12 |
| Email | ashunaukari01@gmail.com |
| Label | Project Request |

https://github.com/Ashgen12

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**Define You**

- GSSOC Participant
- Contributor

# Project Name

Spam Alert System Application using AI, Machine Learning, and Deep Learning Techniques
## Description

-Objective: The objective of this project is to develop a spam alert system application that can detect and filter spam in calls, emails, and SMS using AI, machine learning, and deep learning techniques.

-Background: Spam is a major problem for communication service providers and users. It wastes time and resources and can even be harmful if it contains malicious content. Several machine learning and deep learning techniques have been used to detect and filter spam, including Naïve Bayes, decision trees, neural networks, and random forest.

-Methodology: The proposed spam alert system will use a combination of machine learning and deep learning techniques to detect and filter spam. A pre-trained transformer model like BERT (Bidirectional Encoder Representations from Transformers) can be fine-tuned to detect spam emails from non-spam (HAM). Similar techniques can be used to detect spam in calls and SMS.

-Expected Outcomes: The expected outcome of this project is a fully functional spam alert system application that can accurately detect and filter spam in calls, emails, and SMS.

## Scope

-Research: Conduct research on the latest techniques and algorithms used to detect and filter spam in calls, emails, and SMS.

-Development: Develop a spam alert system application that uses AI, machine learning, and deep learning techniques to accurately detect and filter spam.
## Timeline

Start Date: when assigned
End Date: 20 August