The description of the feature variables are dictated in the word document. The R.code and xls/wf1 datasets are available and ready to be processed. If using EViews instead of R/Python, upload the wf1 dataset to your workspace.
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During this project, I predicted the elastic demand for Golf in the Greater Seattle area, in the form of total rounds of golf played. After some data preprocessing of feature selection, normalization, and correction for Heteroskedasticity, my Multiple Linear Regression model is very accurate with an adjusted R-squared of around 0.86.
ericbrowne/Predicting-Golf-Demand-in-Seattle
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During this project, I predicted the elastic demand for Golf in the Greater Seattle area, in the form of total rounds of golf played. After some data preprocessing of feature selection, normalization, and correction for Heteroskedasticity, my Multiple Linear Regression model is very accurate with an adjusted R-squared of around 0.86.
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