deodel VS ydata-synthetic

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:

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

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.

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!

1: Try to work with what you have and augment your dataset (honestly, 10 points is crap)

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!

The Data-Centric AI Community creates community projects from time to time and is probably willing to help you in your project.

Join nice data communities and start networking.

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 :)

Interesting question! I think our AI/ML devs at the Data-Centric AI Community could have nice perspectives for your to decide :)

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.

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.