Project Overview:

• The "Waiter Tips Prediction Model" is a machine learning project that predicts the amount of tips a waiter can expect to receive based on various factors such as the total bill, customer gender, smoking status, day of the week, time of day, and party size.
• This model can assist waitstaff and restaurants in understanding and estimating tips.

Project Statement:

• The restaurant industry relies on waitstaff tips as a significant part of their income. However, predicting the amount of tips a waiter can expect remains a challenge due to various influencing factors, including the total bill, customer demographics, and dining specifics.
• The aim of this project is to develop a machine learning model that accurately predicts waiter tips, providing valuable insights for optimizing staffing, improving service quality, enhancing customer satisfaction, and making data-driven decisions that benefit both restaurants and waitstaff.
• By addressing this challenge, we seek to empower the hospitality industry with the ability to better understand tipping behavior and enhance operational efficiency.

1. Features
2. Getting Started
3. Usage
4. Data Analysis
5. Machine Learning
6. Model Evaluation
7. Business Value
8. Contributing
9. License
10. Author

• Data Analysis: Explore and visualize the dataset using Pandas and Plotly to gain insights into factors affecting tips.
• Machine Learning: Train a Linear Regression model to predict tips based on selected features.
• Model Evaluation: Assess model performance using metrics like Mean Squared Error (RMSE) and Mean Absolute Error (MAE).

These instructions will help you set up and run the "Waiter Tips Prediction Model" on your local machine for development and testing purposes.

Before running the project, ensure you have the following prerequisites installed:

• Python 3
• Libraries: Pandas, NumPy, Plotly, Scikit-Learn

1. Clone the repository:

   git clone https://github.com/raghavendranhp/waiter_tip_prediction.git

2.Install dependencies 3.Run the project

• Explore the dataset using Pandas to understand its structure and characteristics.
• Visualize data using Plotly to identify relationships between variables.

• Preprocess data by encoding categorical features and splitting it into training and testing sets.
• Train a Linear Regression model to predict tips based on selected features.

• Evaluate the model's performance using metrics like RMSE and MAE.
• Understand the model's accuracy in predicting tips based on provided features.

Optimized Staffing Levels:

• By accurately predicting tips, restaurants can schedule staff more efficiently, ensuring that the right number of waiters is available during peak times and reducing labor costs during slower periods.

Customer Satisfaction Improvement:

• The model can help identify factors that influence tipping behavior, enabling restaurants to focus on improving service quality, enhancing the overall dining experience, and increasing customer satisfaction.

Revenue Maximization:

• Understanding the relationship between the total bill and tips can assist in pricing strategies, upselling, and menu item recommendations that can lead to higher check totals and, subsequently, larger tips.

Performance Benchmarking:

• Restaurants can evaluate the performance of individual waitstaff by comparing predicted tips to actual tips received.
• This can be used to reward high-performing employees and provide training or support to those who may need improvement.

Customer Segmentation:

• The model can uncover patterns related to customer demographics and tipping behavior.
• This information can be used for targeted marketing and personalized services to different customer segments.

Data-Driven Decision-Making:

• The project promotes data-driven decision-making, allowing restaurants to make informed choices regarding pricing, menu selection, employee scheduling, and customer service improvements.

Financial Planning:

• The ability to predict tips accurately can help restaurants with financial planning, including budgeting, forecasting, and ensuring sufficient funds to cover operational costs and salaries.

Competitive Advantage:

• Restaurants can gain a competitive advantage by offering personalized dining experiences based on customer profiles and preferences, resulting in increased customer loyalty.

Service Efficiency:

• The model can help identify bottlenecks in the dining process, allowing restaurants to streamline service and reduce waiting times, which can lead to increased customer satisfaction and potentially larger tips.

Training and Improvement:

• Waitstaff can use the insights from the model to understand which factors influence tips the most, helping them provide better service and enhance their earning potential.

We welcome contributions! If you have suggestions, improvements, or new features to add, please fork the repository and submit a pull request.

This project is under the MIT License, allowing you to use, modify, and distribute the code with certain conditions.

Raghavendran S, Aspiring Data Scientist, Linkedin, [email protected], Happy Analyzing!