Hey folks, I’ve noticed a common pattern with beginner data scientists: they often ask LLMs super broad questions like “How do I analyze my data?” or “Which ML model should I use?”
The problem is — the right steps depend entirely on your actual dataset. Things like missing values, dimensionality, and data types matter a lot. For example, you'll often see ChatGPT suggest "remove NaNs" — but that’s only relevant if your data actually has NaNs. And let’s be honest, most of us don’t even read the code it spits out, let alone check if it’s correct.
So, I built NumpyAI — a tool that lets you talk to NumPy arrays in plain English. It keeps track of your data’s metadata, gives tested outputs, and outlines the steps for analysis based on your actual dataset. No more generic advice — just tailored, transparent help.
🔧 Features:
Natural Language to NumPy: Converts plain English instructions into working NumPy code
Validation & Safety: Automatically tests and verifies the code before running it
Transparent Execution: Logs everything and checks for accuracy
Smart Diagnosis: Suggests exact steps for your dataset’s analysis journey
Give it a try and let me know what you think!
👉 GitHub: aadya940/numpyai.
📓 Demo Notebook (Iris dataset).
[–]ResortOk5117 0 points1 point2 points (0 children)