metadata

title: LLM Tutor
emoji: 👨‍🎓
colorFrom: indigo
colorTo: indigo
sdk: streamlit
sdk_version: 1.41.1
app_file: 2Tutor.py
pinned: false
license: mit
short_description: Learn ML/LLM

Data Science Tutor

Overview

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.

Project Structure

The project is organized into the following main components:

1. Main Application (`Tutor.py`)

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.

2. Pages Directory (`pages/`)

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.

1_Business_understanding.py: Covers the Business Understanding phase.
2_Data_understanding.py: Covers the Data Understanding phase.
3_Data_preparation.py: Covers the Data Preparation phase.
4_Feature_engineering.py: Covers Feature Engineering.
5_Modeling.py: Covers the Modeling phase.
6_Evaluation.py: Covers the Evaluation phase.
7_Deployment.py: Covers Deployment and Testing.
8_Models.py: Covers ML, Deep Learning, and Transformers.

3. Chat Functionality

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.

4. Focus Areas

Users can select specific focus areas from the sidebar to further refine the context of their queries. The focus areas include:

Data Cleaning & Wrangling
Feature Engineering & Selection
Model Selection & Tuning
Interpretability & Explainability
Model Deployment & Monitoring

Installation

To run the application locally, follow these steps:

Clone the repository:

git clone https://github.com/your-username/LLM-Tutor.git
cd LLM-Tutor

Create a virtual environment and activate it:

python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

Install the required dependencies:
```
pip install -r requirements.txt
```
Set up your OpenAI API key:
- Create a .env file in the root directory of the project.
- Add your OpenAI API key to the .env file:
```
OPENAI_API_KEY=your_openai_api_key
```
Run the application:
```
streamlit run Tutor.py
```

Usage

Select a CRISP-DM Step: Use the sidebar to navigate through different steps of the CRISP-DM process.
Ask AI: Enter your OpenAI API key in the sidebar and ask questions related to data science.
Focus Areas: Select specific focus areas to refine the context of your queries.
Interactive Content: Each section includes detailed explanations, key concepts, and quizzes to test your understanding.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contributing

Contributions are welcome! Please read the CONTRIBUTING file for guidelines on how to contribute to this project.

Acknowledgements

pinned: false license: mit short_description: Learn ML/LLM