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The chatbot is designed to classify user input into predefined intents and provide context-aware responses. The solution is scalable, interactive, and suitable for various domains.\n\n---\n\n## **Features**\n- Intent classification using machine learning models.\n- Preprocessing techniques like tokenization, stopword removal, and TF-IDF vectorization.\n- Supports Logistic Regression, Random Forest, and SVM models.\n- User-friendly web-based interface built with Streamlit.\n- Scalable and adaptable for different use cases.\n\n---\n\n## **Technologies Used**\n- **Programming Language**: Python\n- **Libraries**:\n  - `nltk`: For text preprocessing (tokenization, stopword removal).\n  - `scikit-learn`: For machine learning models and evaluation metrics.\n  - `streamlit`: For creating the chatbot's web interface.\n  - `numpy` and `pandas`: For data manipulation and analysis.\n\n---\n\n## **Project Structure**\n```plaintext\nproject-directory/\n│\n├── app.py                  # Main Streamlit app for chatbot\n├── intents.json            # Dataset containing intents, patterns, and responses\n├── chatbot_model.pkl       # Trained machine learning model\n├── vectorizer.pkl          # TF-IDF vectorizer\n├── requirements.txt        # List of dependencies\n├── README.md               # Project documentation\n└── .gitignore              # Ignored files for Git\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsamarthmule%2Fchatbot","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsamarthmule%2Fchatbot","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsamarthmule%2Fchatbot/lists"}