From the course: Python for AI Projects: From Data Exploration to Impact

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Training data pipeline

Training data pipeline - Python Tutorial

From the course: Python for AI Projects: From Data Exploration to Impact

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Training data pipeline

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- [Instructor] Now that we've explored the data and uncovered patterns between user attributes and tool product purchases, it's time to build our training data pipeline, the foundation of any supervised machine learning model. We'll follow a standard scikit-learn workflow, a tried-and-true approach that's widely used across the industry. But before we jump into Python code, let's take a step back. In real-world projects, building the ML training dataset often starts before any code is written. Your raw data might live in a SQL database, a cloud data lake, or even as flat files on an FTP server. You'll often need to join together multiple tables, aggregate behavioral data, such as past bookings or clicks, calculate rolling averages, counts, or ratios, or even generate the target variable itself, for example, figuring out which product a user actually purchased. This data generation step is critical. It ensures that your…

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