📧 SMS / Email Spam Classifier

A machine learning–based web application built with Streamlit that classifies SMS or Email messages as Spam or Ham (Not Spam).
This project demonstrates text preprocessing, feature extraction using TF-IDF, and classification with Multinomial Naive Bayes.

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🌐 Live Demo

👉 Click here to try the app

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🔥 Features

• ✅ Classifies SMS/Email into Spam or Ham in real time
• 🎨 Clean and simple Streamlit web interface
• 🔤 Text preprocessing with NLTK (stopwords removal, stemming, tokenization)
• 📊 Feature extraction using TF-IDF Vectorizer
• 🤖 Machine learning model trained with Multinomial Naive Bayes
• 🚀 Deployment-ready with Streamlit Community Cloud

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⚙️ Project Workflow

1. Data Preprocessing

   • Lowercasing text
   • Removing special characters, punctuation, and stopwords
   • Stemming words

2. Feature Engineering

   • Transform text into numerical vectors using TF-IDF

3. Model Training

   • Trained a MultinomialNB classifier
   • Evaluated with accuracy, precision, and recall

4. Model Persistence

   • Saved trained model and vectorizer as .pkl files using pickle

5. Deployment

   • Interactive UI built with Streamlit
   • Deployed on Streamlit Cloud

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📂 Project Structure Bash

  sms-spam-classifier/
  │
  ├── app.py # Streamlit application
  ├── model.pkl # Trained MultinomialNB model
  ├── vectorizer.pkl # TF-IDF vectorizer
  ├── requirements.txt # Dependencies
  ├── README.md # Project documentation
  └── data/ # (Optional) Dataset files

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🚀 Installation & Setup

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1. Clone the repository

Bash

     git clone https://github.com/<your-username>/sms-spam-classifier.git

cd sms-spam-classifier

2. Create a virtual environment (recommended)

Bash

     python -m venv venv
     source venv/bin/activate   # Mac/Linux
     venv\Scripts\activate      # Windows

3. Install dependencies

Bash

     pip install -r requirements.txt

4. Run the app

Bash

  streamlit run app.py
  streamlit run app.py

📊 Example

Input:

Congratulations! You have won a $1000 Walmart gift card. Click here to claim. Output:

SPAM 🚨

Input:

Hey, are we still meeting for lunch today?

Output:

NOT SPAM ✅

🛠️ Tech Stack

Python 3.8+

Streamlit – UI & Deployment

Scikit-learn – Machine Learning Model

NLTK – Text Preprocessing

Pickle – Model persistence

📌 Requirements

Make sure your requirements.txt includes:

nginx Copy code streamlit scikit-learn nltk pandas numpy

🌍 Deployment

Deployed on Streamlit Community Cloud

Push code & model files (app.py, model.pkl, vectorizer.pkl, requirements.txt) to GitHub

Go to Streamlit Cloud → New App

Select repo & branch → Deploy

📜 License

This project is licensed under the MIT License.