✉️📱 Email/SMS Spam Classifier

A Machine Learning-powered Email and SMS spam classification app built using Python and Streamlit. 🚀

🔍 Overview

This project classifies messages as either "Spam" or "Not Spam" based on their content. It utilizes Natural Language Processing (NLP) techniques to preprocess text before making predictions using a pre-trained model.

📂 Project Structure

The project consists of the following key components:

• 🎨 Streamlit Application – A user-friendly interface for entering and classifying messages.
• 📝 Text Preprocessing – Cleans and processes input text using NLP techniques.
• 🤖 Machine Learning Model – A trained model that predicts whether a message is spam or not.
• 📊 Vectorizer – Converts text data into a numerical format (Bag of Words) for processing.

⚙️ How It Works

1. 📝 Input: The user enters a message (SMS or Email) into the provided text box in the Streamlit app.
2. 🔄 Preprocessing: The text undergoes:
   • Lowercasing
   • Tokenization
   • Removal of non-alphanumeric characters & stopwords
   • Stemming using the Porter Stemmer
3. 🔢 Vectorization: The cleaned text is transformed into a numerical format using a pre-trained CountVectorizer.
4. 🤖 Prediction: The vectorized text is fed into the model, which classifies it as either:
   • 📩 Spam – The message is likely spam.
   • ✅ Not Spam – The message is not spam.
5. 📌 Output: The result is displayed on the Streamlit app.

🚀 Example Usage

1. Run the Streamlit app:

   streamlit run app.py

2. Enter a message in the provided text area.
3. Click the "Predict" button to check if the message is spam or not.

✨ Try These Example Messages:

✅ "Hey, are we still on for dinner tonight?"
📩 "Congratulations! You've won a free ticket to the Bahamas. Call now!"

📦 Dependencies

To install required dependencies, run:

pip install streamlit scikit-learn nltk

Additionally, NLTK data packages punkt and stopwords need to be downloaded.

📁 Files

• app.py – The main script that runs the Streamlit app.
• model.pkl – The pre-trained machine learning model for spam classification.
• vectorizer.pkl – The pre-trained CountVectorizer for text transformation.

🎯 Model Training

The model was trained on a labeled dataset of SMS messages using common text classification techniques, including:

• Text Preprocessing – Cleaning, tokenization, and stemming.
• Vectorization – Converting text into a numerical format using Bag of Words.
• Model Selection – A machine learning classifier was trained and optimized for accurate predictions.

🎉 Conclusion

This project showcases the power of NLP and machine learning in identifying spam messages. The Streamlit app provides a simple interface for testing the classifier with real-world examples.

💡 Feel free to explore, contribute, or extend this project. Happy coding!

📜 License

This project is licensed under the MIT License – see the LICENSE file for details.