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It uses Natural Language Processing (NLP) techniques (Stemming and TF-IDF Vectorization) for text transformation and a trained Bernoulli Naive Bayes Classifier for predictions.\n## Directory Structure:\n```\nDirectory structure:\n└── 04bhavyaa-sms-spam-classification-system/\n    ├── artifacts/\n    │   ├── vectorizer.pkl\n    │   ├── model.pkl\n    │   └── spam.csv\n    ├── app.py\n    ├── sms-spam-classification.ipynb\n    ├── requirements.txt\n    ├── nltk.txt\n    └── README.md\n```\n### Key Features\n- Input a message through the user interface.\n- Classify the message as Spam or Not Spam.\n- Built with Streamlit for the web interface.\n### How It Works\n1. Input Transformation:\n  - Converts the input message to lowercase.\n  - Removes stopwords, punctuation, and non-alphanumeric characters.\n  - Stems the words to their root forms using the Porter Stemmer.\n2. Vectorization:\n  - The transformed text is vectorized using a pre-trained TfidfVectorizer.\n3. 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