Tips Data Analysis — EDA Report

Author: Jenax
Date: 2026-01-13

Overview

This repository contains a short exploratory data analysis (EDA) of the seaborn tips dataset (restaurant bills and tips). The notebook inspects the dataset, summarizes distributions, and visualizes comparisons and relationships between variables.

What I worked on

• Comparison: bar plots / count plots to compare groups (e.g., total_bill by sex).
• Distribution: summary statistics and distribution checks for numeric fields (total_bill, tip, size).
• Relationship: scatter plots and simple visual checks to explore relationships between variables (for example total_bill vs tip).

Files

• tips-data-analyst-report (2).ipynb — primary notebook with EDA, visualizations and notes.
• (Optional) exported CSV/XLSX saved inside the notebook if enabled: tips.csv, tips.xlsx.

How to run

1. Open the notebook tips-data-analyst-report (2).ipynb in Jupyter / JupyterLab / VS Code.
2. Execute cells from top to bottom. The notebook uses seaborn's built-in tips dataset (no external data download required).

Requirements

• Python 3.x
• pandas
• seaborn
• numpy
• matplotlib

You can install the main dependencies with:

pip install pandas seaborn numpy matplotlib

Quick notes / next steps

• The dataset is small and clean (no missing values). One duplicate row was detected.
• Consider statistical tests for group differences (e.g., t-test or non-parametric tests for total_bill by sex).
• Additional visualizations: boxplots, violin plots, heatmap of correlations.
• If you want to produce a shareable report, export the notebook to HTML or PDF.