EDA of Europe Hotel Booking Satisfaction Data
Python
Pandas
Seaborn
Matplotlib
Europe Hotel Booking Satisfaction Analysis
- 📂 Dataset: Europe Hotel Booking Satisfaction Score, 103,904 rows × 17 columns
- 🐍 Language Used: Python
- 🛠 Libraries Used: pandas, seaborn, matplotlib.pyplot
- 📊 Gender Distribution: Female: 50.7%, Male: 49.3%
- 📅 Type of Booking: Group: 47.8%, Individual/Couple: 45.0%, Undefined: 7.2%
- 🧳 Purpose of Travel: Tourism: 30.8%, Academic: 26.2%, Business: 20.4%, Aviation: 13.3%, Personal: 9.2%
- ✨ Top Satisfaction Factor: “Other services” had the highest satisfaction level
- 📶 Lowest Satisfaction Factor: WiFi service received the lowest satisfaction and needs improvement
- 🧑🤝🧑 Gender vs Satisfaction: Males were slightly more satisfied than females by 0.25%
- 📉 Age vs Satisfaction: No strong correlation; satisfaction was evenly spread across all age ranges
- 📋 Data Exploration Techniques: Used
.info() and .describe() for data understanding
- 📌 Data Type Analysis: Majority columns numerical; some categorical (Gender, Type of Booking, etc.)
- 📷 Visualizations Created: Bar plots, distribution charts, scatter plots for trends and patterns
- 🎯 Objective: Identify key trends affecting customer satisfaction in hotel bookings across Europe
- 🧠 Insight Usage: Insights help improve hotel services and target customer preferences better