https://github.com/kolosalai/automl_tutorial
Tutorial using Kolosal AutoML
https://github.com/kolosalai/automl_tutorial
Last synced: 10 months ago
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Tutorial using Kolosal AutoML
- Host: GitHub
- URL: https://github.com/kolosalai/automl_tutorial
- Owner: KolosalAI
- License: mit
- Created: 2025-05-13T02:53:17.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-08-06T13:58:30.000Z (11 months ago)
- Last Synced: 2025-08-06T15:44:21.880Z (11 months ago)
- Language: Jupyter Notebook
- Size: 1.11 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Here's the improved and corrected version of your **Kolosal AutoML Tutorial README**:
---
# 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
```bash
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
```bash
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):
```bash
pip install uv
```
Then install dependencies:
```bash
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
```bash
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.
---
Let me know if you’d like to turn this into a `README.md` file directly or need a version tailored for `docs/`.