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Spark for Data Science by Bikramaditya Singhal, Srinivas Duvvuri

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Regression methods

Regression methods are a type of supervised learning. If the response variable is quantitative/continuous (takes on numeric values such as age, salary, height, and so on), then the problem can be called a regression problem regardless of the explanatory variables' type. There are various kinds of modeling techniques to address the regression problems. In this section, our focus will be on linear regression techniques and some different variations of it.

Regression methods can be used to predict any real valued outcomes. Following are a few examples:

  • Predict the salary of an employee based on his educational level, location, type of job, and so on
  • Predict stock prices
  • Predict buying potential of a customer
  • Predict the time a machine ...

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