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๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐——๐—ฎ๐˜๐—ฎ ๐—ฎ๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜, ๐—ฑ๐—ฟ๐—ผ๐—ฝ ๐˜†๐—ผ๐˜‚๐—ฟ ๐—ฑ๐—ฎ๐˜๐—ฎ ๐—ณ๐—ถ๐—น๐—ฒ, ๐—น๐—ฒ๐˜ ๐˜๐—ต๐—ฒ ๐—Ÿ๐—Ÿ๐—  ๐—ฑ๐—ผ ๐˜๐—ต๐—ฒ ๐—ฎ๐—ป๐—ฎ๐—น๐˜†๐˜€๐—ถ๐˜€ ๐Ÿ“Šโš™๏ธ

In this notebook we will make a data analyst agent: a Code agent armed with data analysis libraries, that can load and transform dataframes to extract insights from your data, and even plots the results!

Letโ€™s say I want to analyze the data from the Kaggle Titanic challenge in order to predict the survival of individual passengers. But before digging into this myself, I want an autonomous agent to prepare the analysis for me by extracting trends and plotting some figures to find insights.

Try it for yourself in this Space demo ๐Ÿ‘‰ https://huggingface.co/spaces/m-ric/agent-data-analyst Read the cookbook to dive deeper ๐Ÿ‘‰ https://huggingface.co/learn/cookbook/agent_data_analyst

Need to make quick exploratory data analysis? โžก๏ธ Get help from an agent.

I was impressed by Llama-3.1โ€™s capacity to derive insights from data. Given a csv file, it makes quick work of exploratory data analysis and can derive interesting insights.

On the data from the Kaggle titanic challenge, that records which passengers survived the Titanic wreckage, it was able by itself to derive interesting trends like โ€œpassengers that paid higher fares were more likely to surviveโ€ or โ€œsurvival rate was much higher for women than menโ€.

The cookbook even lets the agent built its own submission to the challenge, and it ranks under 3,000 out of 17,000 submissions: ๐Ÿ‘ not bad at all!

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