Tips Dataset - Exploratory Data Analysis (EDA)

Objective

This project performs Exploratory Data Analysis (EDA) on the Seaborn Tips dataset using Python, Pandas, Matplotlib, and Seaborn.

Dataset

• Seaborn Built-in Tips Dataset
• Records: 244
• Features: 7

Tools Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn

Questions Answered

• Which day has the highest number of customers?
• Which day has the highest average bill?
• How are bills distributed?
• Is there a relationship between bill and tip?
• Are there outliers?
• Which variables are correlated?

Dashboard

Key Insights

• Saturday has the highest number of customers.
• Weekend bills are generally higher.
• Bills are right-skewed.
• Larger groups tend to spend more.
• Total bill and tip show a positive relationship.

Conclusion

This analysis demonstrates how Seaborn can be used to perform Exploratory Data Analysis and extract meaningful business insights from customer spending patterns.