Hotel Booking Analysis Project

Overview

This project analyzes hotel booking data to understand customer behavior, booking patterns, and cancellation rates. The analysis helps identify business opportunities and operational improvements for hotels.

Dataset

The dataset contains hotel booking information including:

• Booking dates
• Customer details
• Room types
• Cancellation status
• Average daily rate (ADR)
• And other relevant features

Project Structure

1. Data Loading: Importing necessary libraries and loading the dataset
2. Exploratory Data Analysis: Initial examination of data structure and statistics
3. Data Cleaning:
   • Handling missing values
   • Removing outliers
   • Converting data types
4. Visualization: Creating charts to understand key metrics
5. Analysis: Deriving insights from the cleaned data

Key Findings

• Approximately 37% of bookings are canceled
• The dataset contains information on both resort and city hotels
• Significant data cleaning was required to handle missing values and outliers

Technologies Used

• Python
• Pandas (for data manipulation)
• Matplotlib/Seaborn (for visualization)
• Jupyter Notebook (for interactive analysis)

How to Run

1. Clone the repository
2. Install required packages: pip install pandas matplotlib seaborn numpy
3. Open and run the Jupyter Notebook: jupyter notebook Hotel_booking.ipynb

Future Work

• Deeper analysis of cancellation reasons
• Seasonal booking patterns
• Customer segmentation
• Predictive modeling for cancellations

Author

[Pritidarshini Biswal]

License

This project is licensed under the MIT License.