/DEPI_Data_Analytics_R_Project4

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DEPI Data Analytics R Project 4: Hotel Booking Cancellation Analysis

Project Overview

This project aimed to analyze a hotel booking dataset and develop predictive models to forecast booking cancellations. The goal was to uncover insights that could help hotels optimize their operations and revenue management.

Data

The dataset used in this project contained information on hotel bookings, including guest demographics, booking details, and whether the booking was ultimately canceled or not. The dataset was obtained from the DEPI program.

Methodology

The project followed these key steps:

  1. Data Cleaning and Preprocessing: Handled missing values, encoded categorical variables, and engineered new features.
  2. Exploratory Data Analysis: Conducted statistical analysis and visualizations to identify relationships between booking characteristics and cancellation rates.
  3. Insights and Recommendations: Synthesized findings to provide actionable recommendations for hotels to reduce cancellations and improve revenue management.

Key Findings

  • Lead time (the number of days between booking and arrival) was the most influential factor in predicting cancellations. Shorter lead times were associated with higher cancellation rates.
  • Bookings with higher numbers of adults and children had lower cancellation rates compared to solo travelers.
  • Hotels with higher average daily rates (ADR) experienced lower cancellation rates.
  • Predictive models achieved up to 80% accuracy in forecasting booking cancellations.

Conclusions and Recommendations

Based on the analysis, the following recommendations were provided to hotels:

  1. Incentivize longer lead time bookings through dynamic pricing or promotional offers.
  2. Target marketing and sales efforts towards group travelers (families, parties) to capture more low-risk bookings.
  3. Continuously monitor booking patterns and adjust pricing strategies to optimize revenue while managing cancellation risks.

Reflections

This project allowed me to apply various data analytics techniques in a real-world business context. I gained valuable experience in data preparation, exploratory analysis, and building predictive models. The insights generated from this project could help hotels improve their operations and profitability.

Contact

For any questions or comments, please reach out to me at: Email : shashraf214@gmail.com

LinkedIn : https://www.linkedin.com/in/shahd-ashraf-0953a728b/