From Manual EDA to AI-Powered Agents: A Hands-On Experiment with LangChain

Author(s): Sarah Lea

Originally published on Towards AI.

Can an agent take over repetitive EDA tasks? A quick LangChain experiment.

Exploratory data analysis (EDA) is a standard step before we train models or make predictions.

The article explores how LangChain can facilitate the automation of exploratory data analysis (EDA) tasks through AI agents, illustrating the construction of a basic agent designed to identify missing data in CSV files. It underscores the essential components involved in building such agents—including large language models (LLMs), tools, and control logic—while demonstrating their effectiveness in executing data checks and producing structured insights. The experiment highlights the balance between using sophisticated frameworks like LangChain and the simplicity of implementing straightforward Python solutions for common tasks.

Read the full blog for free on Medium.

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Published via Towards AI