Spam Email Detection with Machine Learning 📧

Overview

This project is a machine learning project for detecting spam emails using classification models. It demonstrates the complete workflow of data cleaning, model training, and model evaluation. The goal is to build a system that can automatically classify emails as spam or not spam with high accuracy.

Features

• Preprocess and clean email datasets to remove noise and irrelevant data
• Train multiple classification models to compare performance
• Evaluate models using accuracy, precision, recall, and F1-score
• Use Jupyter notebooks for step-by-step interactive exploration
• Easily extendable for other datasets or email formats

Repository Contents

• cleaned_spam_dataset.csv – Dataset used for training and testing
• 2.py, 3.py, test.py – Python scripts containing different model experiments
• Jupyter/ – Jupyter notebooks with algorithms, data visualization, and analysis
• Rapport_maskininlärningsprojekt_spamdetektion.docx – Project report describing methodology, experiments, and results

🚀 How to Run

1. Clone the repository

git clone https://github.com/Mats914/maskininl-rning.git
cd maskininl-rning