cleanvision
ydata-profiling
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| cleanvision | ydata-profiling | |
|---|---|---|
| 4 | 43 | |
| 921 | 12,053 | |
| 2.4% | 1.5% | |
| 7.3 | 8.5 | |
| 4 days ago | 4 days ago | |
| Python | Python | |
| GNU Affero General Public License v3.0 | 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.
cleanvision
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[D] Is accurately estimating image quality even possible?
Github: https://github.com/cleanlab/cleanvision Blogpost: https://cleanlab.ai/blog/cleanvision/
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How to label augmented images for training YOLO algorithm?
Before you start augmentations you could also use something like CleanVision to examine your image data and see if there are any recurring problems like (near) duplicates, blurry images, etc. It doesn't do anything for you, but it might be good to get an idea for the images you are working with.
- CleanVision: Audit your Image Data for better Computer Vision
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[P] CleanVision: Audit your Image Data for better Computer Vision
Github: https://github.com/cleanlab/cleanvision
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 🚀
What are some alternatives?
imgaug - Image augmentation for machine learning experiments.
dtale - Visualizer for pandas data structures
amplify - Bacalhau Amplify: automatic enrichment, enhancement, and explanation of your data
DataProfiler - What's in your data? Extract schema, statistics and entities from datasets
cleanvision-examples - Notebooks demonstrating example applications of the cleanvision library
dataframe-go - DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration
task-amenability
lux - Automatically visualize your pandas dataframe via a single print! 📊 💡
improved-aesthetic-predictor - CLIP+MLP Aesthetic Score Predictor
get-started-with-JAX - The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
dataprep - Open-source low code data preparation library in python. Collect, clean and visualization your data in python with a few lines of code.