aiqbench takeMachine Learning

This article focuses on structuring end-to-end machine learning projects to demonstrate practical skills like SQL integration and model deployment rather than just theoretical notebook exercises. It emphasizes framing problems around business outcomes as a critical differentiator for data science portfolios in the current hiring market. While it mentions specific tools, the core subject remains the methodology of building robust AI/ML workflows.

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What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Building an End-to-End Data Science Portfolio Project

KDnuggetsAugust 12, 20267 min read

Most portfolios stop at a notebook. Take yours all the way.

The projects that actually get people hired do something different. They start with a business problem and finish with a recommendation, and they show every stage in between. Showing every stage, from raw data to a deployed application, is the thing a resume cannot prove and a notebook cannot fake. To keep it concrete, we'll use one real project the whole way through: the DoorDash Delivery Duration Prediction data project. It's a free project, so you can follow along and build this yourself. We'll work through it inside StrataScratch's built-in notebook environment, an integrated Marimo notebook you can open by clicking "Start Solving" on the project page, so there's nothing to install before you start. So here's what we'll do. We'll take that one project and run it through the nine stages of a real data science project: framing the business problem, pulling the data with SQL,…

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