{"id":21031465,"url":"https://github.com/shanmukhsrisaivedullapalli/smsspamclassification","last_synced_at":"2026-04-11T07:32:12.413Z","repository":{"id":249606763,"uuid":"831983731","full_name":"shanmukhsrisaivedullapalli/SMSSpamClassification","owner":"shanmukhsrisaivedullapalli","description":"SMSSpamClassification is a machine learning project aimed at accurately classifying SMS messages as either spam or ham (non-spam). 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Leveraging the SMSSpamCollection dataset, the project employs Natural Language Processing (NLP) techniques, specifically TF-IDF vectorization, to extract meaningful features from the text data. A Logistic Regression model is trained on these features to build a robust spam detection classifier.\n\nThis repository houses the code for an SMS spam classification project. It encompasses data preprocessing, feature engineering using TF-IDF, model training with Logistic Regression, and model evaluation.\n\n**Project Structure**\n\n```\nSMSSpamClassification/\n├── data/\n│   └── SMSSpamCollection.csv\n├── models/\n│   ├── feature_extraction.pkl\n│   └── spam_detection_model.pkl\n├── notebooks/\n│   └── SMSSpamClassification.ipynb\n├── requirements.txt\n└── README.md\n```\n\n**Data**\n\n* The dataset utilized for this project is the publicly accessible SMS Spam Collection dataset.\n* Raw data is stored in the `data` directory.\n\n**Notebooks**\n\n* **SMSSpamClassification.ipynb**: Contains the entire workflow, including data exploration, preprocessing, feature extraction using TF-IDF, model training with Logistic Regression, and model evaluation.\n\n**Models**\n\n* **feature_extraction.pkl**: Saved TF-IDF vectorizer for future use.\n* **spam_detection_model.pkl**: Trained Logistic Regression model for spam classification.\n\n**requirements.txt**: Lists necessary Python libraries for project execution.\n\n**Installation**\n\n1. Clone the repository:\n   ```bash\n   git clone https://github.com/shanmukhsrisaivedullapalli/SMSSpamClassification.git\n   ```\n2. Create a virtual environment:\n   ```bash\n   python -m venv venv\n   source venv/bin/activate  # or venv\\Scripts\\activate on Windows\n   ```\n3. Install dependencies:\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n**Usage**\n\n1. Run the `SMSSpamClassification.ipynb` notebook to execute the entire project workflow.\n2. The trained model and feature extractor are saved for potential future use.\n\n**Contributing**\n\nContributions are welcome! You can enhance the project by:\n\n* Implementing different NLP techniques or feature engineering methods.\n* Experimenting with various classification algorithms.\n* Improving model performance through hyperparameter tuning.\n* Enhancing the project's documentation.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshanmukhsrisaivedullapalli%2Fsmsspamclassification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshanmukhsrisaivedullapalli%2Fsmsspamclassification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshanmukhsrisaivedullapalli%2Fsmsspamclassification/lists"}