Identifying and distinguishing spam SMS and Email using the multinomial Naïve Bayes model.
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Updated
Nov 22, 2022 - Jupyter Notebook
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
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 is my portfolio website
Data Analysis Projects
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.
AI project for classifying emails as spam or not spam
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