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Data science in Python: pandas, seaborn, scikit-learn


In this video, we'll cover the data science pipeline from data ingestion (with pandas) to data visualization (with seaborn) to machine learning (with scikit-learn). We'll learn how to train and interpret a linear regression model, and then compare three possible evaluation metrics for regression problems. Finally, we'll apply the train/test split procedure to decide which features to include in our model.

This is the sixth video in the series, Introduction to machine learning with scikit-learn. The notebook and resources shown in the video are available on GitHub.

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