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Hotel Booking Analysis Project using Python

Business Problem

Both City Hotel and Resort Hotel have seen significant cancellation rates in recent years, which has resulted in problems including lower revenue and unused hotel room capacity. This project's main goal is to examine hotel reservation cancellations and related variables in order to offer suggestions and insights for lowering cancellation rates and enhancing revenue production.

Assumptions

  • The data is not substantially impacted by unusual occurrences that occurred between 2015 and 2017.
  • The information is up to date and pertinent for effective analysis.
  • Reservations and cancellations are usually made by clients in the same year.
  • 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.

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

A significant percentage of reservations are canceled, as seen by the bar graph, which shows that 37% of reservations are canceled. This has a big effect on hotel earnings. Resort hotels may be more expensive than city hotels, but often have fewer reservations.

Analysis 2: Average Daily Rates

Day-, weekend-, and holiday-based variances in average daily rates between city and resort hotels are depicted in the line graph.

Analysis 3: Monthly Reservation Patterns

Reservation counts by month are shown in a grouped bar graph, with January having the most cancellations and August having the most confirmed and canceled reservations.

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

  • Vighnesh Gannedi