Explore-ML-DS-kaggle-surveys-2020-2021-Saudi-Arabia
ydata-profiling
| Explore-ML-DS-kaggle-surveys-2020-2021-Saudi-Arabia | ydata-profiling | |
|---|---|---|
| 1 | 43 | |
| 0 | 12,085 | |
| - | 1.2% | |
| 5.0 | 8.5 | |
| over 2 years ago | 6 days ago | |
| Jupyter Notebook | Python | |
| - | MIT License |
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Explore-ML-DS-kaggle-surveys-2020-2021-Saudi-Arabia
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State of Web Development 2022
Maybe the biggest hype today is about Web 3.0 which includes blockchain technologies, NFT, Machine Learning, and Virtual Reality. These are all core concepts that would define the Metaverse world. I already started with Machine Learning and had quite a lot of use cases where I could apply it. From the above-mentioned technologies, I think Machine Learning is the one that makes most sense to learn for 2022. For others, it is still too early but they will be a good investment of time in 2023–2024 probably, especially I am very enthusiastic about Virtual Reality. To start with Machine Learning I would recommend starting learning the basic concepts and trying to use different algorithms in Sklearn. One of the great resources you can use with a ton of people that try to help out is Kaggle. I found it a little bit late but it helped me a lot. Kaggle If you want to go deeper into Machine Learning you can check Tensorflow and Kears for creating Neural Networks. But if you are new to ML and want to get something done for a use case you have then probably Sklearn is the better choice. Kaggle also had its survey for 2021 and you can check it out: Kaggle Survey
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?
blog - SZÉKELYDATA | Erdély, Székelyföld és a nagyvilág a Big Data korszakában
dtale - Visualizer for pandas data structures
DataProfiler - What's in your data? Extract schema, statistics and entities from datasets
dataframe-go - DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration
lux - Automatically visualize your pandas dataframe via a single print! 📊 💡
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.
sweetviz - Visualize and compare datasets, target values and associations, with one line of code.
awesome-python - An opinionated list of awesome Python frameworks, libraries, software and resources.
jupyterlab-lsp - Coding assistance for JupyterLab (code navigation + hover suggestions + linters + autocompletion + rename) using Language Server Protocol
lux - 👾 Fast and simple video download library and CLI tool written in Go