README.md

# 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.

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## Question 1: Do cancellation rates differ between hotel types?

![Hotel Type](hotel_type.png)

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 canceled?

![Lead Time](lead_time.png)

The plot shows that canceled bookings tend to have a higher average lead time.

This indicates that bookings made further in advance are less certain and more likely to be canceled.

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## Additional Insight: Cancellation rates across lead time ranges

![Lead Time Groups](lead_time_groups.png)

By grouping lead time into ranges, we can clearly see that cancellation rates increase as lead time increases.

This confirms that the relationship is consistent and not driven only by extreme values.

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## Question 3: Do repeated guests cancel less?

![Repeated Guests](repeated.png)

Repeated guests show lower cancellation rates compared to first-time guests.

This suggests that returning customers are more committed and confident in their bookings.

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## Question 4: How does price (ADR) relate to cancellation behavior?

![ADR](adr.png)

The distribution shows that higher-priced bookings tend to have greater variability and are slightly more likely to be canceled.

Outliers were kept in the dataset but handled carefully in the visualization.

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## Data Cleaning & Decisions
- Duplicate records were identified and removed.
- Missing values were examined but not all were removed, as some represent meaningful absence of information.
- Outliers were identified but kept to preserve real-world behavior.

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## Conclusion
The analysis shows that booking cancellations are influenced by several factors, including lead time, hotel type, customer type, and pricing.

These insights can help businesses better understand customer behavior and improve decision-making.

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## Data Source
The dataset used in this project is based on the "Hotel Booking Demand" dataset, originally available on Kaggle.

The data was cleaned and processed as part of this analysis.
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