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https://github.com/hitthecodelabs/bigquery_ml
Jupyter notebooks that utilize Google BigQuery's machine learning capabilities.
https://github.com/hitthecodelabs/bigquery_ml
bigquery notebooks python sql
Last synced: about 5 hours ago
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Jupyter notebooks that utilize Google BigQuery's machine learning capabilities.
- Host: GitHub
- URL: https://github.com/hitthecodelabs/bigquery_ml
- Owner: hitthecodelabs
- License: mit
- Created: 2024-02-01T04:45:51.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-02-01T04:53:45.000Z (about 1 year ago)
- Last Synced: 2024-07-30T17:22:54.558Z (6 months ago)
- Topics: bigquery, notebooks, python, sql
- Language: Jupyter Notebook
- Homepage:
- Size: 63.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# BigQuery ML Notebooks
Welcome to the "BigQuery ML Notebooks" repository! This repository is dedicated to sharing and collaborating on Jupyter notebooks that utilize Google BigQuery's machine learning capabilities. Whether you're exploring data, building models, or sharing insights, this space aims to serve as a resource for data scientists, analysts, and anyone interested in leveraging BigQuery ML.
## About BigQuery ML
BigQuery ML enables users to create and execute machine learning models in Google BigQuery using standard SQL queries. It brings ML capabilities directly into the data warehouse to allow for seamless integration of ML into data analysis workflows.
## Getting Started
Before diving into the notebooks, ensure you have the following prerequisites covered:
1. **Google Cloud Platform Account**: Ensure you have access to Google Cloud Platform (GCP). You will need it to access BigQuery and run queries.
2. **BigQuery Setup**: Familiarize yourself with BigQuery and ensure your GCP project is set up to use BigQuery.
3. **Jupyter Environment**: Make sure you have a Jupyter environment set up, either locally or through Google Cloud's AI Platform Notebooks.
### Installation
To get started with these notebooks, clone this repository to your local machine or Jupyter environment:
```bash
git clone https://github.com/hitthecodelabs/bigquery_ml.git
```
Navigate into the cloned directory:
bash
```
cd bigquery_ml
```
## Notebooks Overview
This repository contains a variety of notebooks, ranging from introductory examples to more advanced applications of BigQuery ML. Here's a brief overview of what you can expect:
- Introduction to BigQuery ML: Basics of creating and evaluating machine learning models within BigQuery.
- Predictive Analytics with BigQuery ML: Notebooks focused on building predictive models for various use cases.
- Time Series Forecasting: Utilizing BigQuery ML for forecasting future trends based on historical data.
- Text Analytics: Applying BigQuery ML for natural language processing tasks.
## Contributing
We welcome contributions from the community! If you'd like to add your own notebook or improve an existing one, please follow these steps:
1. Fork the repository.
2. Create a new branch for your notebook or changes (git checkout -b my-new-notebook).
3. Commit your changes (git commit -am 'Add some notebook').
4. Push to the branch (git push origin my-new-notebook).
5. Create a new Pull Request.
## License
This project is licensed under the MIT License - see the LICENSE file for details.
Contact
If you have any questions or want to reach out to the maintainers, please open an issue in this repository.