deodel
ydata-synthetic
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deodel | ydata-synthetic | |
---|---|---|
13 | 60 | |
5 | 1,292 | |
- | 4.1% | |
6.3 | 7.3 | |
2 months ago | 2 days ago | |
Python | Jupyter Notebook | |
- | 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.
deodel
- [P] New predictor does classification intermixed with regression
- Easy Machine Learning Dataset Evaluation Tool (Update)
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What are some practical tips for efficiently handling missing or null values in datasets during data analysis in Python?
You could use this new classifier deodel that is very robust. It deals seamlessly with missing data, nulls, mixed numerical and categorical attributes, and multi-class targets. You can see an application with this tool:
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Whatโs your approach to highly imbalanced data sets?
Just to mention that there is also a new algorithm that is immune to the imbalance of data. An implementation in python is available at: - https://github.com/c4pub/deodel
- Robust mixed attributes classifier (machine learning)
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
The deodel classifier can act as a quick dataset evaluation tool. If your data is available in table format, you can check its potential for prediction/classification. Just feed it to deodel. It accepts mixed attributes without any preliminary curation. It simply considers attribute values expressed as floats (dot decimal) as being continuous. It accepts even a mix of continuous and categorical values for the same attribute column.
- [D] Open-source package to mix numerical, categorical and text features?
- [P] Discretization: equal-width trumps equal-frequency?
- [P] Discretization: equal-width beats equal-frequency?
ydata-synthetic
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Coding Wonderland: Contribute to YData Profiling and YData Synthetic in this Advent of Code
Send us your North โญ๏ธ: "On the first day of Christmas, my true contributor gave to me..." a star in my GitHub tree! ๐ต If you love these projects too, star ydata-profiling or ydata-synthetic and let your friends know why you love it so much!
- ydata-synthetic: NEW Data - star count:1083.0
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I absolutely hate my internship
1: Try to work with what you have and augment your dataset (honestly, 10 points is crap)
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Assessing the Quality of Synthetic Data with Data-Centric AI
Data Quality is key for all applications and models, and LLMs are no exception :) I've been working on a small community project with synthetic data (https://github.com/ydataai/ydata-synthetic) using ydata-synthetic, and it really shows! Underrepresentation (category imbalance) and missing data are two of the main issues!
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SOMEBODY HELP ME!
The Data-Centric AI Community creates community projects from time to time and is probably willing to help you in your project.
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Help for Data Scientist position
Join nice data communities and start networking.
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How to become a beast in DS ?
You know what they say: "Tell me who your friends are, and I'll tell you who you are!". Hang out with DS beasts and learn from them :)
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Hey guys, I have a few questions
Interesting question! I think our AI/ML devs at the Data-Centric AI Community could have nice perspectives for your to decide :)
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Embarking on a Journey of 99 Data Science Projects - From Beginner to Expert
Sounds like an amazing journey! Feel free to add your projects on our awesome-python-for-data-science repo as you go! And in case you need a hand or feedback on the projects, we'll be happy to help at the Data-Centric AI Community.
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Data science problems
The best to do is to get started with end-to-end projects in a collaborative environment (somewhat approaching real-world settings). You may find some interesting resources in this GitHub repository. The Data-Centric AI Community actually has a nice support system for this.
What are some alternatives?
dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.
REaLTabFormer - A suite of auto-regressive and Seq2Seq (sequence-to-sequence) transformer models for tabular and relational synthetic data generation.
BotLibre - An open platform for artificial intelligence, chat bots, virtual agents, social media automation, and live chat automation.
DeepRL-TensorFlow2 - ๐ Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
grape - ๐ GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations
Copulas - A library to model multivariate data using copulas.
misc
Conditional-Sig-Wasserstein-GANs
general_class_balancer - Data matching algorithm for categorical and continuous variables
pytorch-forecasting - Time series forecasting with PyTorch
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
gretel-python-client - The Gretel Python Client allows you to interact with the Gretel REST API.