📩 SMS Spam Classifier

Machine Learning model to classify text messages as Spam or Ham using Python and Scikit-learn.

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🔍 Overview

In today's digital world, spam messages clutter inboxes and pose security risks. This project uses a Naive Bayes Machine Learning model to detect spam messages based on their content. The project involves:

• Text preprocessing & feature extraction with TF-IDF
• Building & training a model with scikit-learn
• Evaluating model performance with metrics like accuracy, precision, recall, and F1-score
• (Optional) UI deployment using Streamlit for real-time classification

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🧠 Features

• 📥 Input any SMS message and predict whether it's spam or not
• ✂️ Preprocessing includes stop word removal, punctuation stripping, and tokenization
• 🤖 Trained with Multinomial Naive Bayes for fast and effective classification
• 📊 Performance evaluated with confusion matrix & classification report
• 🧪 Built using real-world SMS spam dataset (UCI Machine Learning Repository)

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🧰 Tools & Technologies

Category	Tools / Libraries
Language	Python 🐍
ML Libraries	Scikit-learn, Pandas, NumPy
Text Processing	NLTK, re
Visualization	Matplotlib, Seaborn 📊
Deployment (optional)	Streamlit 🖥️

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📂 Folder Structure

sms-spam-classifier/
├── data/                   # Dataset (CSV)
├── notebooks/              # Jupyter notebooks
├── spam_classifier.py      # Core model code
├── streamlit_app.py        # (Optional) UI app
├── requirements.txt        # Dependencies
└── README.md               # You’re here!