This project analyzes hotel reservation data to uncover key business insights that can help improve customer experience and increase sales.
We use Python with Jupyter Notebook to explore the dataset, clean the data, visualize important trends, and derive meaningful conclusions.
- Understand reservation patterns across months
- Analyze cancellation rates
- Evaluate average daily rates (ADR) trends
- Provide insights for business decisions
Visualizing how ADR changes month over month helps identify pricing trends and seasonal impacts on bookings.
A pie chart to show the proportion of reservations that were canceled versus those that were not. This helps understand cancellation behavior.
- Python 🐍
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Jupyter Notebook
The data was sourced from a publicly available hotel booking dataset containing information about reservations, cancellation status, pricing, and customer demographics.
- Clone this repository.
- Open the Jupyter Notebook file.
- Run the notebook cells in sequence to reproduce the analysis.
For questions or feedback, reach out to me on LinkedIn.