ai-seed
ydata-profiling
| ai-seed | ydata-profiling | |
|---|---|---|
| 5 | 43 | |
| 113 | 12,053 | |
| 0.0% | 0.9% | |
| 1.8 | 8.5 | |
| about 1 year ago | 8 days ago | |
| Jupyter Notebook | Python | |
| Apache License 2.0 | MIT License |
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.
ai-seed
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Show HN: AutoAI
Thanks for your question. Yes, we did research the space a lot before making AutoAI. Here is what we found:
PyCaret: Semi-automatic. You do the first run; then you figure the next set of runs. Ensemble models require manual configuration.
Tpot: Does a great job. Generates 4-5 lines of py code too. But does not support Neural Networks / DNN. So works only for problems where GOFAI works.
H2O.ai: They have an open-source flavor, but the best way to use it is the enterprise version on the H2O cloud. The interface is confusing, and the final output is black-box.
Now there are many in the enterprise category, such as DataRobot, AWS SageMaker, Azure etc. Most are unaffordable to Data Scientists unless your employer is sponsoring the platform.
AutoAI: This is 100% automated. Uses GOFAI, Neural Networks and DNN, all in one box. It is 100% White-box. It is the only AutoML framework that generates high-quality (1000s of lines) of Jupyter Notebook code. You can check some example codes here: https://cloud.blobcity.com
- [P] Comparison for all Sklearn Classifiers
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Ready AI Code Templates
Hi, this is the team at BlobCity. Creators of A.I. Cloud (https://cloud.blobcity.com). We just released 400+ ready to use AI seed projects. Code templates provide newbie data scientists a great starting reference. We ourselves find them super useful. Let us know what you all think!
- Show HN: Ready code templates for your next AI Experiment
ydata-profiling
- FLaNK 25 December 2023
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First 15 Open Source Advent projects
6. Ydata-synthetic and Ydata-profiling by YData | Github | tutorial
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Coding Wonderland: Contribute to YData Profiling and YData Synthetic in this Advent of Code
Send us your North ⭐️: "On the first day of Christmas, my true contributor gave to me..." a star in my GitHub tree! 🎵 If you love these projects too, star ydata-profiling or ydata-synthetic and let your friends know why you love it so much!
- Data exploration is not dead
- Explore your data in a single line of code
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Which preprocessing steps to improve the performance of a naive bayes classifier
My suggestion start with the EDA - there are a lot of packages that automate that for you already. My usual go-to: https://github.com/ydataai/ydata-profiling.
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Simulating sales data
If you're not sure about the behaviour of your data (i.e., if the original data has properties like seasonality), you can use ydata-profiling to profile your data first.
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I recorded a Data Science Project using Python and uploaded it on Youtube
Super cool! For EDA, you could give ydata-profiling a spin sometime and speed up the process!
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Ydata-Profiling and Dask
Hey guys,
We've been recently at the Dask Demo Day and we're hoping to launch a new feature on ydata-profiling, with the support for Dask dataframes!
We're looking for Dask Wizards to start collaborating on this feature, so if you're interested, please join us to define the roadmap of the project and start making it real
Current GitHub branch is here: https://github.com/ydataai/ydata-profiling/tree/feat/dask
Dedicated dask channel here: https://discord.gg/EHDBuSSDuy
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🧠 ydata-profiling + Dask!
We're looking for Dask Wizards 🧙🏻♂️ to start collaborating on this branch, so if you're interested, please join us to define the roadmap of the project and start making it real 🚀