Using Scikit LLM (Sklearn + ChatGPT) to Simplify Data Science

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Scikit LLM: Data Science Made Easy

Scikit LLM: Data Science Made Easy 🔥

Are you a data science enthusiast looking for a convenient way to leverage powerful machine learning tools? Look no further than Scikit LLM, a combination of Scikit-learn and ChatGPT that makes data science a breeze.

Scikit-learn, also known as sklearn, is a popular machine learning library in Python that provides a wide range of tools for building machine learning models. ChatGPT, on the other hand, is a cutting-edge language model that can generate human-like text based on the input it receives. By combining the two, Scikit LLM offers a seamless and intuitive way to perform data science tasks.

Key Features of Scikit LLM

  • Easy-to-use interface: With Scikit LLM, you can perform various data science tasks using natural language commands, making it accessible even to those with limited programming experience.
  • Fast and efficient: Leveraging the power of Scikit-learn and the language generation capabilities of ChatGPT, Scikit LLM can quickly process and analyze large datasets.
  • Integration with existing workflows: Scikit LLM seamlessly integrates with other Python libraries and tools, allowing you to incorporate it into your existing data science pipeline.
  • Customizable and extensible: You can easily customize and extend the functionality of Scikit LLM to suit your specific data science needs.

Getting Started with Scikit LLM

Ready to give Scikit LLM a try? Here’s a simple example of how you can use it to train a machine learning model:

    
      from scikit_llm import ChatGPTRegressor

      # Load your data
      X, y = load_data('your_dataset.csv')

      # Initialize the ChatGPTRegressor
      regressor = ChatGPTRegressor()

      # Train the model
      regressor.fit(X, y)
    
  

With just a few lines of code, you can train a machine learning model using natural language commands, thanks to the power of Scikit LLM.

Join the Scikit LLM Community

Whether you’re a seasoned data scientist or just getting started, Scikit LLM can help streamline your data science workflows. Join the growing community of Scikit LLM users and unlock the full potential of machine learning with natural language commands.

Ready to take your data science skills to the next level? Try Scikit LLM today and experience the power of easy and intuitive data science.

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@user-qe6ke3el5i
9 months ago

If I have to use Azure Open AI how can I do it .Even though I am using the model

ZeroShotGPTClassifier(openai_model="azure::gpt-3.5-turbo")

I am getting below error

Could not obtain the completion after 3 retries: `InvalidRequestError :: The API deployment for this resource does not exist. If you created the deployment within the last 5 minutes, please wait a moment and try again.`

None

Could not extract the label from the completion: 'NoneType' object is not subscriptable

@ozgurtuncay9815
9 months ago

very helpful, thanks man

@moviemassi
9 months ago

Where do you specify the prompt?

@Sagar-rg3ku
9 months ago

Will it work for predicting the themes in text comments at broad and granular level ?
Where text comment are related to some specific domain or industry

@olegkostromin1065
9 months ago

Hi! I am a co-developer of Scikit-LLM. We would like to thank you for spreading the word about the library. I think you did a great job covering the main concepts behind Scikit-LLM.

I have a quick comment regarding the final performance of the model. I noticed that some of the labels were a bit off (e.g., a clearly positive review was marked as negative). We would recommend not using the labels as-is but transforming them into more descriptive ones. For example, instead of positive, one could transform it into the sentiment of the provided review is positive. In many cases, this could greatly improve overall performance. Unfortunately, we didn't cover this in the documentation well enough, but we will in the next releases and might even provide additional helper classes for label transformations.

Finally, I would like to address anyone watching the video: at the moment, Scikit-LLM is in open-beta. We are actively planning new releases aimed at supporting even more use-cases as well as improving existing functionality. If you like what we are doing, please consider giving us a star on GitHub. Moreover, if you have any questions or suggestions, do not hesitate to reach out.