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

College Project!

Machine learning-based spam detection system for classifying messages or emails as spam or non-spam using NLP techniques.

A repository containing a list of email addresses identified as sources of spam or other types of fraud.

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.

Detect Email/SMS Spam with Machine Learning!

Production-ready spam/ham email classifier with Streamlit app + FastAPI API (Ensemble ML)

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.

ML-powered email spam detector with TF-IDF, Random Forest & OCR for image-based spam. Built on published research (EJASET 2025). Live on Streamlit Cloud.

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

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

This is my portfolio website

Data Analysis Projects

Projects on Data Science Internship

A machine learning-based email spam detection system utilizing NLP techniques to classify emails as spam or ham, enhancing cybersecurity by filtering unwanted messages with high accuracy and efficiency.

Built a classifier to identify spam emails using natural language processing techniques.

AI-powered Email Spam Classifier built with NLP, TF-IDF Vectorization, Multinomial Naive Bayes, and Flask. Achieved 96.86% accuracy on the SMS Spam Collection Dataset.

A Machine Learning project that classifies SMS messages as Spam or Ham using TF-IDF Vectorization and Logistic Regression.

The challenge is to accurately separate spam emails from regular ones using techniques from machine learning models.