Seaborn Tips Dataset Analysis

Project Overview

This project focuses on analyzing the Seaborn's Tips dataset, which contains restaurant billing details such as: - Total bill - Tip amount - Tip percentage - Customer demographics (gender, smoker/non-smoker) - Dining day - Dining time - Group size

The goal is to explore tipping trends and patterns through data analysis and visualization.

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Key Steps

1. Data Cleaning & Manipulation

• Performed using Pandas and NumPy.
• Tasks included handling missing values, creating new features (e.g., tip percentage), and restructuring data for analysis.

2. Exploratory Data Analysis (EDA)

• Conducted with Matplotlib and Seaborn.
• Focused on identifying tipping behavior patterns across demographics, time, and group size.

3. Dashboard Creation

• Built an interactive dashboard in Power BI.
• Provides an intuitive view of insights, allowing filtering and drill-down analysis.
• The dashboard report is in PDF format.

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