Data Science Capstone: Real World ML Decisions (Sandbox)
SPL-213 - Version 1.0.2
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Welcome to the AWS Machine Learning Data Science Capstone: Real World ML Decisions lab where you’ll build, train, and test a machine learning model from the ground up! In this lab you clean data, conduct feature engineering, compare algorithms, and get a firsthand look at how Amazon employees working with machine learning approach ML pipelines.
This lab synthesizes the math-based topics you learned in the Machine Learning Data Scientist path, and you’ll use machine learning to solve a real-life business challenge that the Amazon Studios team faced in the past. This lab is meant to pair with the free digital content for the Machine Learning Data Science Capstone project found here, by selecting your “Learning Library” and searching for “Capstone” https://www.aws.training/learningobject/wbc?id=27201
For the purposes of this lab:
You are assuming the role of a lead data scientist in 2005 and you’re presented with a challenge: Amazon Studios wants to produce award-winning films and, therefore, focus the budget on projects with the best chance of winning those awards. Using the actual dataset from IMDb, an Amazon subsidiary, for movies made between 1990 and 2005, you begin your investigation.
The IMDb dataset is a feature-rich, comprehensive listing of all films released during that time period; it includes critical data such as cast and crew, synopsis, and other production data.
Your task in this lab is to predict which movies will most likely be nominated for an award during the “upcoming” 2005 awards season by building an awards analysis prediction model.
This lab requires:
- Access to a notebook computer with Wi-Fi and Microsoft Windows, Mac OS X, or Linux (Ubuntu, SuSE, or Red Hat)
- The Qwiklabs lab environment is not accessible using an iPad or tablet device, but you can use these devices to access the student guide.
- For Microsoft Windows users: Administrator access to the computer.
- An Internet browser such as Chrome, Firefox, or IE9 (previous versions of Internet Explorer are not supported).
This lab requires approximately 4 hours to complete.
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