jupyter-notebook-chatcompletion
Jupyter Notebook ChatCompletion is VSCode extension that brings the power of OpenAI's ChatCompletion API to your Jupyter Notebooks! (by iterativecloud)
feature-engineering-tutorials
Data Science Feature Engineering and Selection Tutorials (by rasgointelligence)
jupyter-notebook-chatcompletion | feature-engineering-tutorials | |
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2 | 1 | |
6 | 266 | |
- | 1.1% | |
7.9 | 0.0 | |
29 days ago | about 1 month ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | GNU Affero General Public License v3.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
jupyter-notebook-chatcompletion
Posts with mentions or reviews of jupyter-notebook-chatcompletion.
We have used some of these posts to build our list of alternatives
and similar projects.
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Jupyter Notebook ChatCompletion = Notebooks + ChatGPT
You can also generate more code based on your project files - which I also did to generate more commands for the extension.
Cell outputs and problems detected by VSCode can be added to the prompt. You can, for example, feed code into the prompt - which I did to generate the first version of the Readme.
feature-engineering-tutorials
Posts with mentions or reviews of feature-engineering-tutorials.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-03-08.
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How to balance multiple time series data?
I’ve actually solved a similar problem several times in a variety of settings. I’ve had success with boosted trees and feature engineering on the sensor readings over time. I treat each reading as an observation and set the target to be the value I want to forecast (e.g. one hour ahead, the sum over the next day, the value at the same time the next day). There was a recent paper that compared boosted trees to deep learning techniques and found the boosted trees performed really well. Next, I perform feature engineering to aggregate the data up to the current time. These features will include the current value, lagged values over multiple observations for that sensor, more complicated features from moving statistics over different time scales, etc. I actually wrote a blog about creating these features using the open-source package RasgoQL and have similar types of features shared in the open-source repository here. I have also had success creating these sorts of historical features using the tsfresh package. Finally, when evaluating the forecast, use a time based split so earlier data is used to train the model and later data to evaluate the model.
What are some alternatives?
When comparing jupyter-notebook-chatcompletion and feature-engineering-tutorials you can also consider the following projects:
hyde - HyDE: Precise Zero-Shot Dense Retrieval without Relevance Labels
intro-to-python - [READ-ONLY MIRROR] An intro to Python & programming for wanna-be data scientists