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🍽️ WAITER TIPPING PROBLEM 🍽️

Welcome to the "WAITER TIPPING PROBLEM" project! This project utilizes machine learning to predict the tipping behavior of customers based on various dining experience metrics. The core model employs a Decision Tree Regressor to estimate the tip amount given user inputs on various aspects such as food quality, service quality, ambiance, wait time, and the total bill amount.

🌟 Project Overview

The primary goal of this project is to develop a predictive model to help estimate the tip amount a customer might leave at a restaurant. This could be beneficial for:

  • Restaurant staff to better understand and predict tipping behaviors.
  • Customers to gauge an appropriate tip based on their dining experience.

📋 Features

The model takes into account the following features:

  • Food Quality: Rating of the food quality (1-5)
  • Service Quality: Rating of the service quality (1-5)
  • Ambiance: Rating of the restaurant's ambiance (1-5)
  • Wait Time: Rating of the waiting time (1-5)
  • Price: Total bill amount ($)

🛠️ Installation

To run this project, the following libraries are necessary:

  • scikit-learn
  • numpy
  • matplotlib

You can install these libraries using pip:

pip install scikit-learn numpy matplotlib

🚀 Usage

  1. Clone the repository:

    git clone https://github.com/your-username/waiter-tipping-problem.git
cd waiter-tipping-problem

  2. Run the Jupyter Notebook: Open Waiter Tipping Problem.ipynb in Jupyter Notebook or JupyterLab and execute the cells to train the model and make predictions based on user inputs.

📊 Example Code

Here is an example code snippet used in this project:

from sklearn.tree import DecisionLikeRegressor
import numpy as np

# Define features and the target (tip amount)
features = [...]
target = [...]

# Load and prepare data, train the model
...

# Predict tip based on user input
predicted_tip = model.predict(user_features)
print(f"Predicted Tip: ${predicted_tip:.2f}")

🤝 Contributing

Your contributions make this project even better! If you have suggestions or improvements, please open an issue or submit a pull request.

📬 Contact

Feel free to reach out for more information: