Spam SMS Classification

This project demonstrates how to classify SMS messages as spam or ham using Natural Language Processing (NLP) and machine learning models.

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Project Contents

• Spam SMS Classication.ipynb — Jupyter Notebook covering data preprocessing, feature extraction (TF-IDF), model training and evaluation.
• Spam SMS Collection — Dataset containing SMS messages labeled as spam or ham.
• requirements.txt — Python dependencies required to run the notebook.

Features

• Text preprocessing (cleaning, tokenization, stopword removal)
• Feature extraction using Bag of Words and TF-IDF
• Classification models (Naive Bayes, Logistic Regression, SVM)
• Evaluation using Accuracy, Precision, Recall, and F1-score

Dataset

The dataset contains SMS messages labeled as spam or ham, useful for supervised learning in NLP.

Technology Stack

• Python
• scikit-learn
• Pandas, NumPy
• NLTK
• Matplotlib, Seaborn

How to Run

1. Clone or download this repository.
2. Install the dependencies:

   pip install -r requirements.txt

3. Launch Jupyter Notebook:

   jupyter notebook

4. Open and run: Spam SMS Classication.ipynb.

Results

• Models are evaluated using accuracy and F1-score.
• Notebook includes classification metrics and confusion matrix visualizations.

Notes

This project is designed for learning and portfolio showcase. It can be extended with advanced models like LSTMs or Transformers.