Project-1-Python-For-Data-Analysis-Tips

End-to-end EDA project on the Seaborn Tips dataset | Analyzing restaurant tipping patterns using Python, Pandas, Matplotlib & Seaborn

🔍 Exploratory Data Analysis — Tips Dataset

A structured EDA project on the built-in Seaborn tips dataset, completed as part of the Python for Data Analysis course.

📌 Objective

Explore and understand the dataset through statistical summaries and visualizations — covering data structure, distributions, relationships, and key insights.

📂 Dataset

• Source: Seaborn built-in datasets (sns.load_dataset('tips'))
• Records: 244 restaurant bills
• Features: total_bill, tip, sex, smoker, day, time, size

🛠️ Tools & Libraries

Library	Purpose
Pandas	Data loading, exploration, and statistics
NumPy	Numerical operations
Matplotlib	Base plotting
Seaborn	Statistical visualizations

📊 Analysis Structure

1. Imports & Setup
2. Load Dataset
3. First Look (head / tail)
4. Data Structure & Info
5. Missing Values Check
6. Descriptive Statistics
7. Categorical Exploration
8. Univariate Analysis — Numerical
9. Univariate Analysis — Categorical
10. Bivariate Analysis — Numerical vs Numerical
11. Bivariate Analysis — Numerical vs Categorical
12. Bivariate Analysis — Categorical vs Categorical
13. Multivariate Analysis
14. Correlation Heatmap
15. Pairplot
16. Key Insights

💡 Key Insights

• Higher bills are positively correlated with higher tips
• Over 65% of visits occur on weekends (Sat & Sun)
• Party size is a strong driver of total bill amount
• Total bill distribution is right-skewed — most bills fall between $10–$25
• Tip percentage slightly decreases as the bill grows larger

🚀 How to Run

git clone https://github.com/ammarelsayed-2a/Project-1-Python-For-Data-Analysis-Tips.git
cd Project-1-Python-For-Data-Analysis-Tips-
jupyter notebook "Project 1 Tips.ipynb"

👤 Author

Ammar Elsayed — Python for Data Analysis | 2026 LinkedIn