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--- |
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title: LLM Tutor |
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emoji: 👨🎓 |
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colorFrom: indigo |
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colorTo: indigo |
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sdk: streamlit |
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sdk_version: 1.41.1 |
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app_file: 2Tutor.py |
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pinned: false |
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license: mit |
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short_description: Learn ML/LLM |
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--- |
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# Data Science Tutor |
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## Overview |
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The **Data Science Tutor** application is designed to guide users through the CRISP-DM process for data science projects. Each section in the sidebar highlights a different step in the process, providing structured lessons, best practices, and hands-on examples. The application also includes an AI-powered chat feature to assist users with their data science queries. |
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## Project Structure |
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The project is organized into the following main components: |
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### 1. Main Application (`Tutor.py`) |
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The main application file that sets up the Streamlit interface, including the sidebar navigation, chat functionality, and dynamic content loading based on the selected section. |
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### 2. Pages Directory (`pages/`) |
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Contains individual Python scripts for each section of the CRISP-DM process. Each script includes detailed content, explanations, and quizzes related to its respective topic. |
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- `1_Business_understanding.py`: Covers the Business Understanding phase. |
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- `2_Data_understanding.py`: Covers the Data Understanding phase. |
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- `3_Data_preparation.py`: Covers the Data Preparation phase. |
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- `4_Feature_engineering.py`: Covers Feature Engineering. |
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- `5_Modeling.py`: Covers the Modeling phase. |
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- `6_Evaluation.py`: Covers the Evaluation phase. |
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- `7_Deployment.py`: Covers Deployment and Testing. |
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- `8_Models.py`: Covers ML, Deep Learning, and Transformers. |
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### 3. Chat Functionality |
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The application includes an AI-powered chat feature that allows users to ask questions related to data science. The chat model's responses are tailored based on the selected section to provide relevant and focused answers. |
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### 4. Focus Areas |
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Users can select specific focus areas from the sidebar to further refine the context of their queries. The focus areas include: |
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- Data Cleaning & Wrangling |
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- Feature Engineering & Selection |
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- Model Selection & Tuning |
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- Interpretability & Explainability |
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- Model Deployment & Monitoring |
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## Installation |
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To run the application locally, follow these steps: |
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1. Clone the repository: |
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```sh |
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git clone https://github.com/your-username/LLM-Tutor.git |
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cd LLM-Tutor |
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``` |
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2. Create a virtual environment and activate it: |
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```sh |
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python -m venv venv |
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source venv/bin/activate # On Windows, use `venv\Scripts\activate` |
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``` |
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3. Install the required dependencies: |
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```sh |
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pip install -r requirements.txt |
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``` |
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4. Set up your OpenAI API key: |
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- Create a [.env](http://_vscodecontentref_/1) file in the root directory of the project. |
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- Add your OpenAI API key to the [.env](http://_vscodecontentref_/2) file: |
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``` |
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OPENAI_API_KEY=your_openai_api_key |
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``` |
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5. Run the application: |
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```sh |
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streamlit run Tutor.py |
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``` |
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## Usage |
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- **Select a CRISP-DM Step**: Use the sidebar to navigate through different steps of the CRISP-DM process. |
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- **Ask AI**: Enter your OpenAI API key in the sidebar and ask questions related to data science. |
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- **Focus Areas**: Select specific focus areas to refine the context of your queries. |
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- **Interactive Content**: Each section includes detailed explanations, key concepts, and quizzes to test your understanding. |
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## License |
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This project is licensed under the MIT License. See the [LICENSE](http://_vscodecontentref_/3) file for more details. |
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## Contributing |
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Contributions are welcome! Please read the CONTRIBUTING file for guidelines on how to contribute to this project. |
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## Acknowledgements |
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- [Streamlit](https://streamlit.io/) |
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- [OpenAI](https://www.openai.com/) |
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- [CRISP-DM](https://www.sv-europe.com/crisp-dm-methodology/) |
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--- |
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pinned: false |
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license: mit |
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short_description: Learn ML/LLM |
