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Waiters Tip Prediction Using Machine Learning

1MAMIDI TARANI, 2ADARI JAYANTH

1Assistant Professor, 2MCA Final Semester,

2Master of Computer Applications,

2Sanketika Vidya Parishad Engineering College, Vishakhapatnam, Andhra Pradesh, India

ABSTRACT

This project aims to predict the amount of tip a customer might give at a restaurant using machine learning. We use a Linear Regression model that is trained on real restaurant data. The model takes various input features such as the total bill amount, the gender of the customer, whether the customer smokes or not, the day of the week, the time of the meal (lunch or dinner), and the size of the group. By analyzing these features, the model learns how each factor influences the tip amount. Once trained, it can predict the tip based on new customer information. This prediction can help restaurants in many ways. It can improve customer service by understanding which situations lead to better tips, support managers in making better decisions using data, and help staff performance by identifying what impacts tipping behavior. It can also be used in automated systems to suggest expected tips, making the billing process smarter. Overall, this project shows how machine learning can be applied in the food industry to understand customer behavior and enhance business strategies

IndexTerms:Tip Prediction, Linear Regression, Machine Learning, Restaurant Analytics, Customer Behavior, Bill Amount, Smoking Status, Mealtime (Lunch/Dinner), Party Size, Data-Driven Insights, Service Improvement, Predictive Modeling, Restaurant Management.