This tutorial introduces the essential steps of data handling and preprocessing in machine learning using Python. It covers how to load and clean datasets, handle missing values, encode categorical features, scale numerical data, and properly split data into training, validation, and test sets. You’ll also learn how to build robust and reusable preprocessing workflows using scikit-learn pipelines, ensuring clean, reproducible, and leak-free machine learning projects.

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Quiz

This quiz evaluates your understanding of data handling and preprocessing concepts in machine learning, including dataset loading, data cleaning, missing value treatment, encoding, scaling, and the use of scikit-learn pipelines.

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