College Project!

Identifying and distinguishing spam SMS and Email using the multinomial Naïve Bayes model.

One of the primary methods for spam mail detection is email filtering. It involves categorize incoming emails into spam and non-spam. Machine learning algorithms can be trained to filter out spam mails based on their content and metadata.

In this repository, I have done simple python projects for understanding the python environment.

Detect Email/SMS Spam with Machine Learning!

Projects on Data Science Internship

Email Spam Tool is a powerful application designed for testing and analyzing email systems by generating and sending bulk emails. This tool is meant for security professionals and developers to evaluate email filtering systems and anti-spam measures.

This is my portfolio website

Data Analysis Projects

We receive emails that are not advantageous to us and can be misleading and dangerous; We have no idea what damage is lurking behind them. This project assists us in avoiding potentially hazardous emails by screening them.

This project was developed during an internship at Afame Technologies, where I worked as a Machine Learning Intern. The goal of this project is to create a model that can accurately detect spam emails using a Naive Bayes classifier. The model achieves an impressive 98% accuracy on the spam detection dataset.

This project utilizes machine learning to address the broad problem of spam through algorithms like Multinomial Naive Bayes and Logistic Regression; it can classify incoming emails as either spam or ham. This project aims to enhance email security and user experience while minimizing the risks of phishing attacks.

Data Science Projects in Python: From Beginner to Advanced Levels

AI project for classifying emails as spam or not spam

This repository contains a comprehensive project on detecting email spam using machine learning techniques. The goal of the project is to classify emails as spam or not spam by training models on a dataset of email messages.