# 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.
---
## 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.
---
## Question 2: Are bookings with longer lead time more likely to be…
# 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.
---
## 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.
---
## 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.
---
## Question 2: Are bookings with longer lead time more likely to be…
# 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.
---
## 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.
---
## 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.
---
## Question 2: Are bookings with longer lead time more likely to be…
