Forcasting Spam Emails

We need to find a way to get rid of spammers.
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
3. Usage
4. Contact
5. Acknowledgements

About The Project

Hackers are always waiting to see who their next victim is – the office worker who clicks on a link thinking it is a work related email or perhaps a teenager who receives an email stating they can receive a free smartphone in exchange for some personal information. All of a sudden, this innocent scenario turns into a harmful virus or theft of one’s identity. There are many ways stolen identification can occur, but one of the most common ways is through spam emails. Our goal is to forecast which incoming email messages are spam and when we have likelihood to respond to them.

Built With

• Python 3
• IDLE (comes with Python)

Getting Started

To get a local copy up and running, download the avg_spam_confidence.py and daily_email_count.py Python files and the text input files, mbox.txt, mbox-07.txt, and mbox-short.txt into the same folder. Then run the code in an IDE software, such as IDLE, or in Command Prompt. The avg_spam_confidence.py file isolates the numeric portion of a text file to output the average spam confidence. The daily_email_count.py file counts emails that are sent out on a particular day of the week.

Usage

Using the mbox.txt file in the avg_spam_confidence.py program in IDLE:

Using the mbox-07.txt file in the daily_email_count.py program in IDLE:

Using the mbox-short.txt file in the daily_email_count.py program in Command Prompt:

>>python3 daily_email_count.py
>>Enter the file name: mbox-short.txt
>>{'Sat': 1, 'Fri': 20, 'Thu': 6}

Contact

Karishma Mathur - [email protected]

Project Link: https://github.com/Mathurkarishma/forecast-spam-email

Acknowledgements

• Dr. Prasanna Menta at University of Maryland, Global Campus - Fall 2019