📧 Spam Email Detection with Machine Learning

Welcome to the Spam Email Detection Project — a machine learning solution to classify emails as spam or not using natural language processing (NLP) techniques and the Multinomial Naive Bayes algorithm.

---

🚀 Overview

The goal of this project is to build an efficient, lightweight model to enhance email filtering systems, reducing spam and improving user productivity.

Dataset Used: Spam Email Dataset (Kaggle)

---

🔧 Tools & Technologies

• Python: Core programming language
• Scikit-learn: For model training and evaluation
• Pandas & NumPy: Data manipulation
• Matplotlib & Seaborn: Visual analysis and plotting

---

📂 Key Features

1. Data Preprocessing:

   • Cleaned and tokenized text
   • Removed noise (stopwords, punctuations, etc.)

2. Model Building:

   • Utilized Multinomial Naive Bayes, ideal for text classification
   • Trained/test split to evaluate generalization

3. Evaluation:

   • Accuracy, Precision, Recall, F1-Score

4. Notebook Implementation:

   • Explore full code on Kaggle:
     Email Spam Classification using Multinomial NB

---

📈 Results

The model achieved strong classification performance, proving effective for practical spam detection tasks. Check the linked notebook for full evaluation metrics and insights.

---

💡 Why It Matters

Spam emails remain a significant issue—wasting time and posing security risks. This project demonstrates how machine learning can help mitigate these problems through smart, automated filtering.

---

Happy Learning! 🚀