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Built entirely using **Flask**, the chatbot allows users to ask questions about game economy, map strategies, skins, and more.  \n\nThe backend leverages **TF-IDF vectorization** and **Logistic Regression** for intent classification, ensuring precise and relevant responses.  \n\n---\n\n## 🚀 Key Features  \n1. **Flask-Powered Chat Interface**:\n   - The entire chatbot runs on Flask, handling both frontend and backend.  \n   - Users can type questions and receive instant responses.  \n   - Supports queries about game economy, map strategies, skins, and general gameplay tips.    \n2. **Intent Classification with Machine Learning**:  \n   - Utilizes **TF-IDF (Term Frequency-Inverse Document Frequency)** for feature extraction.  \n   - **Logistic Regression** model classifies user queries into predefined categories.  \n   - Ensures fast and accurate response generation.  \n\n---\n\n## 🔍 How It Works  \n1. **User Input**:  \n   - Users enter their queries via the Flask-based chat interface (e.g., \"What is the best economy strategy for Mirage?\").  \n2. **Backend Processing**:  \n   - The input text is processed using **TF-IDF vectorization**.  \n   - The **Logistic Regression model** predicts the intent of the query.  \n   - A relevant response is retrieved from the predefined knowledge base.  \n3. **Response Generation**:  \n   - Flask sends the generated response back to the user via the chat interface.  \n\n---\n\n## 🛠 System Requirements  \n### Dependencies  \n- Python 3.8+  \n- Required Libraries:  \n  ```bash\n  pip install flask nltk pandas scikit-learn\n  \n---\n\n## 📄 License  \nThis project is licensed under the MIT License. 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