Hotel Booking Data Analysis and Visualization

A comprehensive data analysis project examining hotel booking patterns, cancellation behaviors, and revenue dynamics using real-world hospitality industry data. This project demonstrates end-to-end data science workflows, from data cleaning and exploratory analysis to advanced visualization and actionable business insights.

📊 Project Overview

This analysis explores a dataset of 119,390 hotel bookings across two property types (City Hotel and Resort Hotel) to uncover patterns in guest behavior, seasonal trends, and factors influencing booking cancellations. The insights generated can help hotel managers optimize pricing strategies, reduce cancellation rates, and improve operational efficiency.

Key Highlights

• Dataset Size: 119,390 bookings with 32 features
• Analysis Period: July 2015 - August 2017
• Property Types: City Hotels and Resort Hotels
• Cancellation Rate: 37% overall (41.73% for City Hotels, 27.76% for Resort Hotels)

🎯 Business Questions Addressed

1. What factors contribute to booking cancellations?

   • Lead time, deposit requirements, previous cancellations, and special requests all show significant correlation with cancellation likelihood

2. How do seasonal patterns affect bookings and revenue?

   • Peak seasons identified with distinct patterns for city vs. resort properties
   • Revenue optimization opportunities during high-demand periods

3. What guest segments generate the most value?

   • Analysis of guest demographics, booking channels, and stay patterns
   • Identification of high-value customer segments

4. How can hotels optimize their pricing and distribution strategies?

   • Channel-specific performance analysis
   • Price sensitivity and demand elasticity insights

🛠️ Technologies Used

• Python 3.x
• Pandas - Data manipulation and analysis
• NumPy - Numerical computations
• Matplotlib - Static visualizations
• Seaborn - Statistical data visualization
• Jupyter Notebook - Interactive development environment

📁 Project Structure

Hotel-Booking-Data-Analysis-and-Visualization/
│
├── Hotel Bookings.csv           # Raw dataset
├── hotel_bookings_analysis.ipynb # Main analysis notebook
├── technical_report.pdf          # Detailed technical documentation
├── README.md                     # Project documentation
└── requirements.txt              # Python dependencies

🚀 Getting Started

Prerequisites

Ensure you have Python 3.7 or higher installed on your system.

Installation

1. Clone the repository

   git clone https://github.com/thonos-cpu/Hotel-Booking-Data-Analysis-and-Visualization.git
   cd Hotel-Booking-Data-Analysis-and-Visualization

2. Install required packages

   pip install pandas numpy matplotlib seaborn jupyter

   Or install from requirements file:

   pip install -r requirements.txt

3. Launch Jupyter Notebook

   jupyter notebook hotel_bookings_analysis.ipynb

📈 Analysis Methodology

1. Data Cleaning and Preprocessing

• Handled missing values in critical columns (country, agent, company)
• Removed statistical outliers in ADR (Average Daily Rate)
• Created derived features for enhanced analysis
• Ensured data type consistency

2. Exploratory Data Analysis (EDA)

• Univariate Analysis: Distribution of key variables
• Bivariate Analysis: Relationships between features
• Temporal Analysis: Seasonal and yearly trends
• Categorical Analysis: Hotel type, market segment, distribution channel

3. Advanced Visualizations

• Correlation heatmaps for feature relationships
• Time series analysis of bookings and revenue
• Geographic distribution of guest origins
• Cancellation pattern analysis

4. Statistical Insights

• Hypothesis testing for cancellation factors
• Revenue analysis by segment and season
• Lead time impact assessment
• Guest behavior clustering

🔍 Key Findings

Cancellation Patterns

• Bookings with zero deposit show dramatically higher cancellation rates (99.5%)
• Lead time is strongly correlated with cancellations (longer lead times = higher cancellation probability)
• Guests with previous cancellations are more likely to cancel again
• Special requests are associated with lower cancellation rates (14.15% vs. 46.11%)

Revenue Insights

• City Hotels generate higher overall revenue but show greater volatility
• Resort Hotels demonstrate strong seasonal patterns with summer peaks
• Direct bookings and corporate channels show lower cancellation rates
• Weekend bookings command higher ADR in city properties

Guest Behavior

• Couples (2 adults, 0 children) represent the largest booking segment
• Portuguese guests make up the largest demographic (27.5%)
• Repeat guests show 14% cancellation rate vs. 38% for first-time bookers
• Average lead time: 104 days

📊 Sample Visualizations

The analysis includes:

• Monthly booking trends with cancellation overlays
• ADR distribution by hotel type and season
• Guest origin heatmaps
• Lead time vs. cancellation probability scatter plots
• Market segment performance comparisons
• Correlation matrices for key variables

🎓 Learning Outcomes

This project demonstrates proficiency in:

• Data cleaning and preprocessing techniques
• Exploratory Data Analysis (EDA) best practices
• Statistical analysis and hypothesis testing
• Data visualization principles
• Business insight generation from data
• Technical documentation and reporting

📝 Future Enhancements

• Predictive Modeling: Build machine learning models to predict cancellations
• Customer Segmentation: K-means clustering for guest profiling
• Time Series Forecasting: ARIMA models for demand prediction
• Interactive Dashboard: Streamlit or Plotly Dash implementation
• A/B Testing Framework: Experimental design for pricing strategies

📄 Documentation

For detailed technical analysis, methodology, and comprehensive findings, please refer to the Technical Report.

👤 Author

thonos-cpu

Feel free to reach out for questions, suggestions, or collaboration opportunities!

🙏 Acknowledgments

• Dataset sourced from hotel booking records (2015-2017)
• Inspired by real-world hospitality industry challenges
• Built as part of a data analysis and visualization curriculum

📜 License

This project is available for educational and portfolio purposes. Please provide attribution if you use or adapt this work.

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