OBJECTIVES:

• Review advanced manipulation techniques and EDA
• Introduce Linear Regression and OLS models
• Transform data to Linear Like relationships
• Implement Linear Regression with scikitlearn
• Evaluate Linear Regression with RMSE
• Develop Regression models using the train/test split paradigm
• Evaluate models using cross-validation

• Python for Data Analysis by Wes McKinney.
• Data Science Handbook: Section on Aggregation and Grouping
• An Introduction to Statistical Learning: See chapter on Linear Regression.
• Andrew Ng: Introduction to Linear Regression Video lecture from Stanford ML class.
• Elements of Statistical Learning: More rigorous mathematics, see introduction to Linear Regression.