email-spam-classifier

A simple yet effective machine learning model that classifies emails as Spam or Not Spam (Ham) using Multinomial Naive Bayes and TF-IDF vectorization. Built using Python and scikit-learn.

📧 Email Spam Classifier using Multinomial Naive Bayes

This project is a machine learning model built to classify emails as Spam or Not Spam (Ham) using Multinomial Naive Bayes and TF-IDF vectorization. It serves as a basic example of text classification using NLP techniques.

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🧠 Features

• Preprocesses email text: lowercasing, punctuation removal, stopwords removal
• Converts text data into numerical format using TF-IDF
• Trains a Multinomial Naive Bayes classifier
• Predicts whether an email is spam or not
• Shows classification report & accuracy

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📁 Dataset

The project uses a labeled dataset containing spam and ham messages.
Example:

Label	Message
ham	"Hey, how are you?"
spam	"Congratulations! You won!"

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📦 Libraries Used

• pandas
• scikit-learn
• matplotlib (optional for visualization)

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🛠️ How It Works

1. Load the dataset
2. Preprocess the text (tokenization, cleaning, etc.)
3. Convert text to vectors using TfidfVectorizer
4. Train the Multinomial Naive Bayes model
5. Evaluate the model using accuracy & classification report
6. Predict whether new messages are spam or not

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🚀 Run the Project

1. Clone the repository:

   git clone https://github.com/yourusername/email-spam-classifier.git
   cd email-spam-classifier