📩 SMS Spam Classifier

An end-to-end NLP project to classify SMS messages as Spam or Ham (Not Spam) using traditional machine learning, TF–IDF features, and a Streamlit web interface.

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🚀 Overview

This project demonstrates a full ML workflow:

1. Data ingestion from Kaggle’s SMS Spam Collection Dataset
2. Text preprocessing with NLTK (cleaning, stopword removal, stemming)
3. Feature extraction using TF–IDF
4. Model training & evaluation with Logistic Regression (and optional Naive Bayes)
5. Model persistence with joblib
6. Interactive web app built with Streamlit
7. Deployment-ready for Streamlit Community Cloud

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📊 Dataset

• Name: SMS Spam Collection Dataset
• Source (Kaggle): https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset
• Instances: ~5.5k SMS messages labeled as ham or spam

Please refer to the dataset page for licensing and citation details.

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🧰 Tech Stack

• Language: Python
• Libraries:
  • pandas, numpy
  • scikit-learn (TF–IDF, Logistic Regression, Naive Bayes, metrics)
  • nltk (stopwords, stemming)
  • joblib (model serialization)
  • streamlit (web app)

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📁 Project Structure

sms-spam-classifier/
├── app.py                     # Streamlit app
├── requirements.txt           # Python dependencies
├── README.md                  # Project documentation
├── data/
│   └── spam.csv               # Kaggle dataset (placed here by you)
├── models/
│   ├── spam_model.pkl         # Trained Logistic Regression model
│   └── vectorizer.pkl         # TF–IDF vectorizer
└── notebooks/
    └── sms_spam_classifier.ipynb  # Training & evaluation notebook