Abstract

The objective of this paper is to build a linear model for predicting the average amount of tip in dollars a waiter is expected to earn from the restaurant given the predictor variables, i.e., total bill paid, day, the gender of the customer (sex) time of the party, smoker and size of the party. The model was based on the data created by one waiter at a certain restaurant in California who recorded information about each tip he received. This model can be applied at any restaurant with similar predictor variables to determine the amount of tip. The final result from this analysis proved a regression model with a minimum prediction Root Mean Square Error (RMSE) of 1.1815.

Keywords: Machine learning, Linear regression, Mean Squared Error (MSE), Root Mean Squared Error (RMSE)