Spam SMS Classification

A clean, end-to-end machine learning project for classifying SMS messages as spam or ham (not spam) using Python. The repository includes a reproducible workflow from data loading and EDA through model training and evaluation.

🧰 Tech Stack

• Python (pandas, numpy, scikit-learn, matplotlib, seaborn)
• Jupyter Notebook

📂 Repository Structure

.
├── Spam SMS Classication.ipynb     # Main notebook (EDA → preprocessing → modeling → evaluation)
├── README.md
├── requirements.txt
├── LICENSE
├── .gitignore
└── data/                           # (optional) place raw data here or link in notebook

If your dataset is external, add a short note or a download script in the notebook showing how to fetch it.

🚀 Getting Started

1) Create a virtual environment

python -m venv .venv
# Windows
.venv\Scripts\activate
# macOS/Linux
source .venv/bin/activate

2) Install dependencies

pip install -r requirements.txt

3) Run the notebook

jupyter notebook "Spam SMS Classication.ipynb"

or with JupyterLab:

jupyter lab

🧪 What the Notebook Covers

• Exploratory Data Analysis (class balance, message length stats, word clouds if applicable)
• Text cleaning (lowercasing, punctuation/stopword removal, stemming/lemmatization if used)
• Feature engineering (e.g., TF-IDF or CountVectorizer)
• Model training (e.g., Logistic Regression, SVM, Naive Bayes)
• Evaluation (Accuracy, Precision, Recall, F1-score, Confusion Matrix, ROC-AUC)

📊 Example Results

Add a screenshot or table of your best model metrics here, e.g.

Model               Accuracy  Precision  Recall  F1
LogisticRegression  0.98      0.98       0.97    0.98

(Replace with your actual results.)

🔄 Reproducibility

• Set a fixed random_state where applicable.
• Note any preprocessing steps and parameters inside the notebook.
• Keep dataset path configurable (e.g., via a variable like DATA_PATH).

📝 License

This project is released under the MIT License. See LICENSE for details.

🙌 Acknowledgements

• Dataset: (Add dataset source link if you used one, e.g., UCI SMS Spam Collection)