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.

Download the notebook: https://github.com/justmarkham/scikit...
pandas installation instructions: http://pandas.pydata.org/pandas-docs/...
seaborn installation instructions: http://seaborn.pydata.org/installing....
Longer linear regression notebook: https://github.com/justmarkham/DAT5/b...
Chapter 3 of Introduction to Statistical Learning: http://www-bcf.usc.edu/~gareth/ISL/
Videos related to Chapter 3: https://www.dataschool.io/15-hours-of...
Quick reference guide to linear regression: https://www.dataschool.io/applying-an...
Introduction to linear regression: http://people.duke.edu/~rnau/regintro...
pandas Q&A video series: https://www.dataschool.io/easier-data...
pandas 3-part tutorial: http://www.gregreda.com/2013/10/26/in...
pandas read_csv documentation: http://pandas.pydata.org/pandas-docs/...
pandas read_table documentation: http://pandas.pydata.org/pandas-docs/...
seaborn tutorial: http://seaborn.pydata.org/tutorial.html
seaborn example gallery: http://seaborn.pydata.org/examples/in...

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