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https://github.com/shubhamdeepkeshav/visualization-on-tips

📊 Data visualization project analyzing tipping behavior in restaurants using Python. 🍽️ Explores insights based on ⏰ time, 👥 party size, 🧑‍🤝‍🧑 gender, and 🚬 smoker status with Matplotlib and Seaborn.
https://github.com/shubhamdeepkeshav/visualization-on-tips

data-visualization dataanalysis eda matplotlib python seaborn

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📊 Data visualization project analyzing tipping behavior in restaurants using Python. 🍽️ Explores insights based on ⏰ time, 👥 party size, 🧑‍🤝‍🧑 gender, and 🚬 smoker status with Matplotlib and Seaborn.

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README 

        
# 🍽️ Tips Dataset Visualization Project



Welcome to the Tips Dataset Visualization repository! This project is all about exploring and visualizing tipping behavior through various data visualization techniques. Dive in to see how different factors like bill amounts, gender, and time of day influence tipping! 📊



# 📂 Dataset Overview

The Tips dataset used in this project provides insights into restaurant bills and tips, including the following features:



💵 total_bill: Total bill in dollars

💰 tip: Tip amount in dollars

🧑 sex: Gender of the individual paying

🚬 smoker: Whether the individual is a smoker

📅 day: Day of the week

🕑 time: Time of the day (Lunch or Dinner)

👥 size: Number of people in the party



# 🎨 Visualizations

This project includes a range of visualizations to help uncover patterns and trends in tipping behavior. Here's a quick rundown of the visuals you’ll find:



📊 Bar Charts: Analyzing tips by categories like gender and smoker status.

📉 Histograms: Displaying the distribution of total bills and tips.

🔵 Scatter Plots: Examining the relationship between total bill and tip amounts.

📈 Box Plots: Comparing tipping behavior across different days and times (Lunch vs. Dinner).

🥧 Pie Charts: Summarizing categorical data for a clear overview.

These visualizations are created using popular Python libraries like Matplotlib and Seaborn for clean and insightful data presentation.



# ⚙️ Prerequisites

Before running the notebook, make sure you have the necessary Python libraries installed:



    pip install pandas matplotlib seaborn



# 🚀 Usage

Clone the Repository: Clone this repository to your local machine.



    git clone https://github.com/shubhamdeepkeshav/tips-visualization.git

Navigate to the Project Directory:



    cd tips-visualization

           

Open the Notebook: Start Jupyter Notebook to view and interact with the analysis.



    jupyter notebook "visualization on tips dataset.ipynb"



# 📊 Key Insights

The analysis provides insights into how factors like gender, smoking status, and time of day affect tipping. For instance, you may discover trends like:



Dinner times might see higher average tips than lunch 🍽️

Tipping behavior varies between smokers and non-smokers 🚬

Different days of the week may also influence how much people tip 📆

Explore the notebook to see these trends come to life! 🔍



# 🤝 Contributing

Contributions are welcome! If you have ideas to make this project even better, feel free to fork the repository and submit a pull request. Let’s make data visualization more fun together! 🎉