Here's the improved and corrected version of your Kolosal AutoML Tutorial README:

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Kolosal AutoML Tutorial

This repository demonstrates how to use Kolosal AutoML to train, evaluate, and explain a regression model using the California Housing dataset.

🚀 What You'll Learn

• How to load and prepare data
• How to train a model using Kolosal AutoML
• How to evaluate model performance
• How to generate a performance report
• How to generate model explainability insights

📦 Getting Started

1. Clone the Repository

git clone https://github.com/Genta-Technology/automl_tutorial.git
cd automl_tutorial

2. Set Up the Environment Using uv

a. (Optional) Create a Virtual Environment

python -m venv venv
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

b. Install uv and Sync Dependencies

Install uv (a faster pip replacement):

pip install uv

Then install dependencies:

uv pip sync requirements.lock.txt

uv is a fast dependency manager that automatically uses pyproject.toml for locking and syncing environments.

3. Run the Jupyter Notebook

jupyter notebook tutorial.ipynb

📁 Files

• tutorial.ipynb – Main notebook tutorial
• requirements.lock.txt – Locked dependency versions
• README.md – This file

✅ Requirements

• Python 3.8+
• Jupyter Notebook
• Kolosal AutoML
• scikit-learn
• uv for dependency management

📄 License

This project is licensed under the MIT License.

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Let me know if you’d like to turn this into a README.md file directly or need a version tailored for docs/.