a maching learning model to automating the detection of spam in emails

In this notebook, I have created a SPAM and HAM filter predictions on the dataset Spam ham collection from UCI repository

This jupyter notebook has various ML classification models to detected a mail as spam or not

Spam/Ham Classifier creation and its deployment using Flask, Frontend creation using Flasgger and URL generation using Ngrok [In Colab Notebook]

This repository contains a Jupyter Notebook that demonstrates the implementation of Support Vector Machines (SVM) for spam detection. The notebook includes data preprocessing, feature extraction, training the SVM model, and evaluating its performance.

The notebook covers basic data cleaning, exploration, and visualization techniques to understand the characteristics of spam emails.

Natural LangWiz is a repository for exploring Natural Language Processing (NLP) techniques through Jupyter notebooks. It covers everything from text preprocessing and sentiment analysis to advanced transformer models. Dive in to see how we turn raw text into actionable insights with a touch of NLP wizardry!

A Multinomial Naive Bayes model for classifying emails as spam or ham, featuring a Jupyter Notebook for analysis and a Streamlit app for interactive use.

This repository contains all the projects I completed during my internship at SoftNetix. Each project is organized into folders and notebooks to ensure clarity and ease of understanding.

This project classifies SMS messages into spam or ham using NLP techniques. It includes data preprocessing, feature extraction with TF-IDF, and classification using models like Naive Bayes, Logistic Regression, and SVM. The notebook also compares model performance using evaluation metrics, providing insights into effective SMS spam detection.