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Chapter 9: Predicting Continuous Target Variables with Regression Analysis

Chapter Outline

  • Introducing regression
    • Simple linear regression
  • Exploring the Ames Housing Dataset
    • Loading the Ames Housing dataset into a data frame
    • Visualizing the important characteristics of a dataset
  • Implementing an ordinary least squares linear regression model
    • Solving regression for regression parameters with gradient descent
    • Estimating the coefficient of a regression model via scikit-learn
  • Fitting a robust regression model using RANSAC
  • Evaluating the performance of linear regression models
  • Using regularized methods for regression
  • Turning a linear regression model into a curve - polynomial regression
    • Modeling nonlinear relationships in the Ames Housing dataset
    • Dealing with nonlinear relationships using random forests
      • Decision tree regression
      • Random forest regression
  • Summary

Please refer to the README.md file in ../ch01 for more information about running the code examples.