{"id":19830018,"url":"https://github.com/2003harsh/sms-spam-classifier","last_synced_at":"2026-06-09T02:10:24.060Z","repository":{"id":183712318,"uuid":"669810591","full_name":"2003HARSH/Sms-Spam-Classifier","owner":"2003HARSH","description":"ML model for spam detection using Naive Bayes \u0026 TF-IDF. Achieved 0.98 accuracy. Utilized Scikit-learn, Numpy, nltk. Implements NLP concepts. 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The application is built using Streamlit for a user-friendly experience.\n\n## Key Achievements\n- **High Accuracy:** Achieved an outstanding accuracy score of 0.98.\n- **Precision:** Boasted a precision score of 0.991, showcasing the model's reliability.\n- **Technology Stack:** Utilized Scikit-learn, Pandas, Numpy, NLTK, Matplotlib, Seaborn, WordCloud, Streamlit, and more.\n- **NLP Expertise:** Gained proficiency in NLP concepts like Tokenization, stopword removal, stemming, term frequency-inverse document frequency, etc.\n\n## How to Use\n1. **Clone Repository:**\n   ```\n   git clone https://github.com/your-username/spam-classifier.git\n   cd spam-classifier\n   ```\n\n2. **Install Dependencies:**\n   ```\n   pip install -r requirements.txt\n   ```\n\n3. **Run the Streamlit App:**\n   ```\n   streamlit run app.py\n   ```\n\n4. **Access the App:**\n   Open your browser and go to `http://localhost:8501`.\n\n5. **Input:**\n   Provide the text you want to classify.\n\n6. **Output:**\n   The app will predict whether the input is spam or legitimate.\n\nThis project demonstrates excellence in spam detection, leveraging Streamlit for an interactive and seamless user experience. #MachineLearning #NLP #SpamClassification #Streamlit 🤖📤📥\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F2003harsh%2Fsms-spam-classifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2F2003harsh%2Fsms-spam-classifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F2003harsh%2Fsms-spam-classifier/lists"}