This project focuses on analyzing hotel booking data to uncover insights about reservation cancellations and understand the key factors that affect customer behavior and hotel revenue.
The goal is to explore patterns in booking data, visualize trends, and provide actionable recommendations that can help hotels reduce cancellations and improve customer retention.
- Identify the main factors influencing reservation cancellations.
- Compare cancellation rates between city and resort hotels.
- Analyze seasonal trends and price fluctuations.
- Provide practical recommendations to reduce cancellations.
- Python 🐍
- Pandas – Data cleaning and manipulation
- Matplotlib – Data visualization
- Seaborn – Statistical data visualization
- Jupyter Notebook – Interactive analysis environment
- 🏙️ City hotels have more bookings but also higher cancellation rates than resort hotels.
- 💸 Higher prices are strongly correlated with higher cancellation rates.
- 📅 August had the most bookings, while January recorded the highest number of cancellations.
- 📊 Price and timing are the most significant factors influencing customer cancellation behavior.
- Adjust pricing strategies during high-demand seasons.
- Offer discounts or promotions during months with high cancellation rates.
- Launch targeted marketing campaigns in January to recover potential revenue losses.
The detailed report can be found here:
Analysis p.pdf
.