📧 Spam Detector Classifier

This project is an end-to-end Spam Message Classifier that uses NLP techniques like text preprocessing and TF-IDF vectorization, along with classification models like Multinomial Naive Bayes. It's built in Python and deployed using Streamlit for an interactive web app.

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🚀 Features

• Detects whether a given message is Spam or Not Spam (Ham)
• Text preprocessing (tokenization, stopword removal, stemming)
• TF-IDF based feature extraction
• 13+ ML algorithms tested (Logistic Regression, SVM, Random Forest, etc.)
• Advanced ensemble techniques: Voting Classifier and Stacking Classifier
• Performance comparison using accuracy and precision
• Multinomial Naive Bayes (MNB) selected as final model due to highest precision
• Deployment-ready with pickle serialization
• Interactive web interface via Streamlit

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🛠️ Technologies Used

• Python 3.x
• Pandas
• NumPy
• NLTK
• Scikit-learn
• Streamlit
• Matplotlib / Seaborn (for EDA)
• Pickle

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📁 Project Structure

📦 spam-detector/
├── app.py # Streamlit app
├── model.pkl # Final trained MultinomialNB model
├── vectorizer.pkl # Trained TF-IDF vectorizer
├── spam.csv # Dataset (SMS Spam Collection)
├── requirements.txt # List of dependencies
└── README.md # Project description

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✅ Setup Instructions

1. Clone the repository

   git clone https://github.com/tahirkorma/spam-detector.git
   cd spam-detector
   

2. Create and activate a virtual environment

   # Create venv
   python -m venv venv
   

3. Install dependencies

   pip install -r requirements.txt
   

4. Run the app

   streamlit run app.py
   

🚀 Live Demo

🎯 Check out the live working app here:

👉 Click to Open Live Spam Detector

💡 You can input your own text and instantly see if it's classified as Spam or Not Spam.