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Multiple linear Regression with Automated Backward Elimination (with p-value and adjusted r-squared)
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Multiple linear regression model implementation with automated backward elimination (with p-value and adjusted r-squared) in Python and R for showing the relationship among profit and types of expenditures and the states.
To make our model reliable and select the features that have an impact on the output, we use Backward elimnation. Since standard R2 is biased,for better performance and model implementation, adjusted r-squared is included in the estimation.
This method allowed to narrow down the dataset features (expenditure types) to 2 and icreasethe regression model score from 0.93 to 0,95.
Implemeting the model in R has shown a bit different results: only one feature (expenditure type) is estimated to be statistically significant.
