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Hotel Booking Analysis Project using Python

Business Problem

In recent years, both City Hotel and Resort Hotel have been facing high cancellation rates, leading to issues such as reduced revenue and underutilized hotel room capacity. The primary objective of this project is to analyze hotel booking cancellations and associated factors to provide insights and recommendations for reducing cancellation rates and improving revenue generation.

Assumptions

  • Unusual events between 2015 and 2017 do not significantly impact the data.
  • The data is current and relevant for efficient analysis.
  • Anticipated negative consequences of implementing the suggested techniques are minimal.
  • The hotels are not currently using the proposed solutions.
  • Booking cancellations have a significant impact on revenue generation.
  • Clients typically make reservations and cancellations within the same year.

Research Questions

  1. What factors influence hotel reservation cancellations?
  2. How can hotel reservation cancellations be minimized?
  3. How can hotels make informed pricing and promotional decisions?

Hypotheses

  • Cancellation rates increase with higher prices.
  • Longer waiting lists are associated with more frequent cancellations.
  • Offline travel agents contribute more to reservations than online channels.

Analysis and Findings

Analysis 1: Reservation Cancellation Rates

The bar graph reveals that a substantial portion of reservations are canceled, with 37% of bookings being canceled. This has a significant impact on hotel revenue. Resort hotels have fewer bookings compared to city hotels, possibly due to higher pricing.

Analysis 2: Average Daily Rates

The line graph illustrates fluctuating average daily rates between city and resort hotels, with variations based on days, weekends, and holidays.

Analysis 3: Monthly Reservation Patterns

A grouped bar graph displays reservation counts by month, showing August with the highest number of reservations (both confirmed and canceled) and January with the most cancellations.

Analysis 4: Price and Cancellations

The bar graph indicates a correlation between higher prices and increased cancellations, suggesting that accommodation cost plays a vital role in cancellations.

Analysis 5: Cancellations by Country

The pie chart identifies Portugal as the country with the highest number of reservation cancellations.

Analysis 6: Booking Sources

The table outlines booking sources, highlighting online travel agencies (47%) and groups (27%) as primary contributors. Direct bookings account for 4%.

Analysis 7: Price and Cancellation Relationship

The graph reinforces the relationship between higher average daily rates and increased cancellations.

Suggestions

  1. Implement dynamic pricing strategies to reduce cancellations due to high prices.
  2. Provide weekend and holiday discounts for resort hotels to improve occupancy rates.
  3. Launch marketing campaigns in January to counter the highest cancellation rates.
  4. Enhance hotel quality and services in Portugal to mitigate cancellations.

Usage

  1. Clone this repository.
  2. Install the required Python packages.
  3. Run the provided Python scripts for data analysis.

Contributors

  • Ananwita Sarkar