📱 SMS Spam Filtering via Text Mining and Supervised Learning ✉️🤖

SMS-Spam-Filtering-via-Text-Mining-and-Supervised-Learning is a machine learning project designed to automatically detect and filter spam messages from genuine ones (ham). By combining text mining techniques with supervised learning algorithms, this project demonstrates how Natural Language Processing (NLP) can be applied to improve communication security and user experience.

✨ Key Features

✉️ Spam Detection: Classify SMS messages into Spam or Ham (Not Spam)

🧹 Text Preprocessing: Tokenization, stopword removal, stemming/lemmatization, and vectorization

🔠 Feature Extraction: TF-IDF, Bag of Words (BoW), and n-grams representation

🧠 Supervised Learning Models: Naïve Bayes, Logistic Regression, SVM, Random Forest, and Deep Learning models

📊 Model Evaluation: Accuracy, Precision, Recall, F1-score, ROC-AUC

📈 Visualization: Word clouds, confusion matrices, feature importance plots

⚡ Real-Time Filtering: Deployable as a service or integrated into messaging apps

🧰 Tech Stack

Programming: Python 🐍

Machine Learning: Scikit-learn, XGBoost, TensorFlow/Keras (optional for deep models)

NLP Libraries: NLTK, spaCy

Data Handling: Pandas, NumPy

Visualization: Matplotlib, Seaborn, WordCloud

📁 Project Structure 📁 dataset/ # SMS Spam Collection Dataset 📁 preprocessing/ # Text cleaning & feature extraction scripts 📁 models/ # Supervised ML algorithms 📁 notebooks/ # Jupyter notebooks with experiments 📁 results/ # Model performance, plots & confusion matrices 📁 app/ # Deployment-ready script (Flask/Streamlit)

🚀 Getting Started git clone https://github.com/yourusername/SMS-Spam-Filtering-via-Text-Mining-and-Supervised-Learning.git cd SMS-Spam-Filtering-via-Text-Mining-and-Supervised-Learning pip install -r requirements.txt jupyter notebook

📌 Use Cases

📱 Messaging Apps: Prevent spam SMS from reaching users

📡 Telecom Industry: Automated spam filtering for carriers

🔐 Security: Reduce phishing attacks and fraudulent SMS campaigns

🎓 Learning: Understand how NLP + ML work together for classification problems

🤝 Contributing

Contributions are welcome! You can add new NLP techniques, optimize models, or deploy the system as a microservice.

📜 License

MIT License – Free to use for research, learning, and open-source contributions.

⭐ Support

If you like this project, consider giving it a star ⭐ to support open-source work in NLP & spam detection.