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-learn-videos pandas installation instructions: http://pandas.pydata.org/pandas-docs/stable/install.html seaborn installation instructions: http://seaborn.pydata.org/installing.html Longer linear regression notebook: https://github.com/justmarkham/DAT5/blob/master/notebooks/09_linear_regression.ipynb 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-expert-machine-learning-videos/ Quick reference guide to linear regression: https://www.dataschool.io/applying-and-interpreting-linear-regression/ Introduction to linear regression: http://people.duke.edu/~rnau/regintro.htm pandas Q&A video series: https://www.dataschool.io/easier-data-analysis-with-pandas/ pandas 3-part tutorial: http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/ pandas read_csv documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html pandas read_table documentation: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_table.html seaborn tutorial: http://seaborn.pydata.org/tutorial.html seaborn example gallery: http://seaborn.pydata.org/examples/index.html WANT TO GET BETTER AT MACHINE LEARNING? HERE ARE YOUR NEXT STEPS: 1) WATCH my scikit-learn video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A 2) SUBSCRIBE for more videos: https://www.youtube.com/dataschool?sub_confirmation=1 3) JOIN "Data School Insiders" to access bonus content: https: