Application of Linear Regression to Real Estate

Archit Kumar1


1Rocking Horse Ct, Dublin, California, United States.

Abstract: Linear regression is a technique to create a model from given data, where the data is a set of examples and the information from each example consists of a set of features and a target variable. The model can then be used to predict the target variables of new examples from their features. We review this technique and apply it to a problem in business to illustrate how it can help making good business decisions.
Keywords: Machine learning, Supervised learning, Linear regression.


Cite this article as: Archit Kumar, Application of Linear Regression to Real Estate, Int. J. Math. And Appl., vol. 9, no. 3, 2021, pp. 125-128.

References
  1. Andriy Burkov, The hundred-page machine learning book, Volume 1, Andriy Burkov Canada, (2019).
  2. George AF Seber and Alan J Lee, Linear regression analysis, Volume 329, John Wiley & Sons, (2012).
  3. Xin Yan and Xiaogang Su, Linear regression analysis: theory and computing, World Scientific, (2009).
  4. Cha Zhang and Yunqian Ma, Ensemble machine learning: methods and applications, Springer, (2012).

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