# Hotel Booking Cancellation Analysis
## Overview
This project explores hotel booking data in order to understand the main factors that influence booking cancellations.
The goal is to identify patterns in customer behavior and provide insights into what makes a booking more likely to be canceled.
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## Dataset
The dataset is based on the "Hotel Booking Demand" dataset from Kaggle.
It includes over 100,000 observations and features such as:
- Lead time
- Hotel type
- Customer type
- Average daily rate (ADR)
- Cancellation status
The dataset was cleaned and prepared before analysis.
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## Question 1: Do cancellation rates differ between hotel types?
The results show that city hotels have higher cancellation rates compared to resort hotels.
This suggests that bookings in city hotels may be less stable, possibly due to differences in customer behavior or booking flexibility.
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## Question 2: Are bookings with longer lead time more likely to be…
# Hotel Booking Cancellation Analysis
## Overview
This project analyzes hotel booking data to better understand what drives cancellations.
The goal was to explore patterns in customer behavior and identify factors that make a booking more or less likely to be canceled. Rather than focusing only on description, the analysis aims to provide insights that could be useful from a business perspective.
---
## Dataset
The dataset is based on the "Hotel Booking Demand" dataset.
It includes over 100,000 booking records with features such as:
- Lead time (how early the booking was made)
- Hotel type (city or resort)
- Customer type (repeated or new)
- Average daily rate (ADR)
- Cancellation status
The dataset is large enough to reveal meaningful patterns, while still being manageable for exploratory analysis.
---
## Data Preparation & Cleaning
Before starting the analysis, the dataset was reviewed and cleaned:
- Duplicate records were identified and removed to avoid bias in the results…
# Hotel Booking Cancellation Analysis
## Overview
This project analyzes hotel booking data to better understand what drives cancellations.
The goal was to explore patterns in customer behavior and identify factors that make a booking more or less likely to be canceled. Rather than focusing only on description, the analysis aims to provide insights that could be useful from a business perspective.
---
## Dataset
The dataset is based on the "Hotel Booking Demand" dataset.
It includes over 100,000 booking records with features such as:
- Lead time (how early the booking was made)
- Hotel type (city or resort)
- Customer type (repeated or new)
- Average daily rate (ADR)
- Cancellation status
The dataset is large enough to reveal meaningful patterns, while still being manageable for exploratory analysis.
---
## Data Preparation & Cleaning
Before starting the analysis, the dataset was reviewed and cleaned:
- Duplicate records were identified and removed to avoid bias in the results…
