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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