The primary goal of this project is to analyze hotel booking cancellations in order to identify the variables affecting them and provide actionable insights to reduce cancellation rates. By understanding the factors influencing cancellations, hotels can optimize their strategies to improve revenue generation and increase efficiency in room utilization.
- Business Problem
- Assumptions
- Research Questions
- Hypothesis
- Code Implementation
- Suggestions
In recent years, both City Hotel and Resort Hotel have experienced high cancellation rates, leading to revenue loss and suboptimal room utilization. Lowering cancellation rates is crucial for improving efficiency and revenue generation for both hotels.
- No unusual occurrences between 2015 and 2017 significantly impact the data.
- The data used for analysis is current and applicable.
- There are no unforeseen negatives to implementing recommended solutions.
- The hotels are not currently employing any of the suggested techniques.
- Booking cancellations are the primary factor affecting revenue effectiveness.
- Cancellations result in vacant rooms for the booked duration.
- Clients make reservations and cancellations within the same year.
- Cancellation rates increase with higher prices.
- Longer waiting lists lead to more cancellations.
- Majority of clients book through offline travel agents.
Three main research questions formulated to guide the analysis:
- Variables affecting hotel reservation cancellations.
- Strategies to improve hotel reservation cancellations. -Assistance in pricing and promotional decisions for hotels. -Hypothesis: Three hypotheses proposed regarding factors influencing cancellation rates, setting a foundation for the analysis.
- Importing Libraries: Importing necessary libraries for data analysis.
- Loading the Dataset: Reading the dataset containing hotel booking information.
- Exploratory Data Analysis & Data Cleaning:
- Performing exploratory data analysis (EDA) to understand the dataset structure and identify any missing values.
- Cleaning the data by handling missing values, removing outliers, and converting data types.
- Data Analysis and Visualization: -Visualizing cancellation percentages through bar graphs to understand the impact on hotel revenues. -Comparing reservation counts between different hotel types using count plots. -Analyzing average daily rates (ADR) in City and Resort hotels over time. -Examining reservation status per month and ADR trends to identify patterns. -Visualizing cancellation rates in the top 10 countries to understand geographical impacts. -Exploring customer segments and their booking preferences.
- Actionable recommendations based on the analysis findings, aimed at reducing cancellation rates and optimizing revenue generation.
- Adjust pricing strategies to mitigate cancellations.
- Offer discounts during peak periods to incentivize bookings.
- Implement marketing campaigns to address high cancellation months.
- Focus on improving service quality, especially in regions with high cancellation rates.
The readme file provides an overview of the project's objectives, contents, and key findings. By following the outlined analysis steps and recommendations, hotels can gain insights into mitigating cancellation rates and enhancing their revenue generation strategies.